{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent models in DisMod-MR without many different types of data\n",
    "\n",
    "In DisMod-II there was a requirement to have at least three different data types, corresponding to different parts of the compartmental model.  DisMod-MR can run a compartmental model with only two, or even one data type, but this requires expert priors to fill in the gaps.\n",
    "\n",
    "This document provides an example of how a model for Parkinson's Disease might look with different subsets of data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt, numpy as np\n",
    "import dismod_mr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {}\n",
    "#iter=101; burn=0; thin=1  # use these settings to run faster\n",
    "iter=10_000; burn=5_000; thin=5  # use these settings to make sure MCMC converges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent fit with all data\n",
    "\n",
    "Let's start with a consistent fit of the simulated PD data.  This includes data on prevalence, incidence, and SMR, and the assumption that remission rate is zero.  All together this counts as four different data types in the DisMod-II accounting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 43 rows of data\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.load('pd_sim_data/')\n",
    "model.keep(areas=['GBR'], sexes=['female', 'total'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: all-cause mortality data not found, using m_all = .01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ihme/homes/abie/.local/lib/python3.6/site-packages/pandas/core/indexing.py:480: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "fitting submodels\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ihme/homes/abie/.local/lib/python3.6/site-packages/pandas/core/indexing.py:480: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n",
      "CPU times: user 26min 23s, sys: 2.23 s, total: 26min 25s\n",
      "Wall time: 30min 22s\n"
     ]
    }
   ],
   "source": [
    "model.setup_model()\n",
    "%time model.fit(iter=iter, burn=burn, thin=thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models['p, i, r, smr'] = model\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'p, i, r, smr': <dismod_mr.data.ModelData at 0x7fc296336160>}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent fit without incidence\n",
    "\n",
    "Now let's do it again with the incidence removed.  Since there is data on prevalence and SMR as well as the assumption that remission is zero, this counts as three data types, the minimum allowed for DisMod-II."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 43 rows of data\n",
      "kept 39 rows\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.load('pd_sim_data/')\n",
    "model.keep(areas=['GBR'], sexes=['female', 'total'])\n",
    "\n",
    "model.input_data = model.input_data[model.input_data.data_type != 'i']\n",
    "print('kept %d rows' % len(model.input_data.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: all-cause mortality data not found, using m_all = .01\n",
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "fitting submodels\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n",
      "CPU times: user 22min 16s, sys: 1.1 s, total: 22min 17s\n",
      "Wall time: 22min 51s\n"
     ]
    }
   ],
   "source": [
    "model.setup_model()\n",
    "%time model.fit(iter=iter, burn=burn, thin=thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models['p, r, smr'] = model\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent fit without incidence or mortality\n",
    "\n",
    "This uses only prevalence data and the assumption that there is no remission, so it is not valid in DisMod-II.  The Bayesian priors included by default in DisMod-MR make it possible, but the tradeoff between incidence and mortality is not informed by any data in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 43 rows of data\n",
      "kept 36 rows\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.load('pd_sim_data/')\n",
    "model.keep(areas=['GBR'], sexes=['female', 'total'])\n",
    "\n",
    "model.input_data = model.input_data[model.input_data.data_type == 'p']\n",
    "print('kept %d rows' % len(model.input_data.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: all-cause mortality data not found, using m_all = .01\n",
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "fitting submodels\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n",
      "CPU times: user 17min 23s, sys: 1.8 s, total: 17min 25s\n",
      "Wall time: 17min 24s\n"
     ]
    }
   ],
   "source": [
    "model.setup_model()\n",
    "%time model.fit(iter=iter, burn=burn, thin=thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the above took 20 minutes in 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models['p, r'] = model\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent fit with only prevalence\n",
    "\n",
    "Now without assumption of zero remission, DisMod-MR is going for a very underconstrained problem, and relies on the priors heavily.  However, the prevalence data is there, so the estimates of prevalence will not be changed much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 43 rows of data\n",
      "kept 36 rows\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.load('pd_sim_data/')\n",
    "model.keep(areas=['GBR'], sexes=['female', 'total'])\n",
    "\n",
    "model.input_data = model.input_data[model.input_data.data_type == 'p']\n",
    "print('kept %d rows' % len(model.input_data.index))\n",
    "\n",
    "model.set_level_bounds('r', 0., 1.)\n",
    "model.set_level_value('r', age_before=0., age_after=101., value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: all-cause mortality data not found, using m_all = .01\n",
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "fitting submodels\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n",
      "CPU times: user 17min 21s, sys: 2.21 s, total: 17min 24s\n",
      "Wall time: 17min 23s\n"
     ]
    }
   ],
   "source": [
    "model.setup_model()\n",
    "%time model.fit(iter=iter, burn=burn, thin=thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models['p'] = model\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of alternative models\n",
    "\n",
    "Let's compare the distributions for all of these now.  You can see that the more data there is, the more concentrated the posterior distribution becomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, (label, model) in enumerate(models.items()):\n",
    "    plt.hist(model.vars['p']['mu_age'].trace().mean(1), density=True, histtype='step',\n",
    "         color=dismod_mr.plot.colors[i%4], linewidth=3, linestyle=['solid','dashed'][i//4],\n",
    "         label=label)\n",
    "plt.legend(loc=(1.1,.1))\n",
    "plt.title('Posterior Distribution Comparison\\nCrude Prevalence');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, (label, model) in enumerate(models.items()):\n",
    "    plt.hist(model.vars['i']['mu_age'].trace().mean(1), density=True, histtype='step',\n",
    "         color=dismod_mr.plot.colors[i%4], linewidth=3, linestyle=['solid','dashed'][i//4],\n",
    "         label=label)\n",
    "plt.legend(loc=(1.1,.1))\n",
    "plt.title('Posterior Distribution Comparison\\nCrude Incidence');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent fit without prevalence\n",
    "\n",
    "The really challenging case is without any prevalence data. DisMod-MR will go for it, but there will be a lot of uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 43 rows of data\n",
      "kept 7 rows\n",
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: all-cause mortality data not found, using m_all = .01\n",
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.load('pd_sim_data/')\n",
    "model.keep(areas=['GBR'], sexes=['female', 'total'])\n",
    "\n",
    "model.input_data = model.input_data[model.input_data.data_type != 'p']\n",
    "print('kept %d rows' % len(model.input_data.index))\n",
    "\n",
    "model.setup_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting submodels\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n",
      "CPU times: user 17min 37s, sys: 1.55 s, total: 17min 39s\n",
      "Wall time: 17min 37s\n"
     ]
    }
   ],
   "source": [
    "%time model.fit(iter=iter, burn=burn, thin=thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models['i, r, smr'] = model\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent fit with incidence only\n",
    "\n",
    "DisMod-MR it will even go for it with _only_ incidence.  But that is not ideal..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 43 rows of data\n",
      "kept 4 rows\n",
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: all-cause mortality data not found, using m_all = .01\n",
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.load('pd_sim_data/')\n",
    "model.keep(areas=['GBR'], sexes=['female', 'total'])\n",
    "\n",
    "model.input_data = model.input_data[model.input_data.data_type == 'i']\n",
    "print('kept %d rows' % len(model.input_data.index))\n",
    "model.set_level_bounds('r', 0., 1.)\n",
    "\n",
    "model.setup_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting submodels\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      "Cannot calculate AIC: float division by zero\n",
      ". Cannot calculate AIC: float division by zero\n",
      ". Cannot calculate AIC: float division by zero\n",
      ". Cannot calculate AIC: float division by zero\n",
      ". Cannot calculate AIC: float division by zero\n",
      ". Cannot calculate AIC: float division by zero\n",
      ". . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n",
      "CPU times: user 12min 51s, sys: 2.12 s, total: 12min 53s\n",
      "Wall time: 12min 51s\n"
     ]
    }
   ],
   "source": [
    "%time model.fit(iter=iter, burn=burn, thin=thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models['i'] = model\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistent fit without prevalence or incidence\n",
    "\n",
    "DisMod-MR is not magic, however.  Without prevalence _or_ incidence, it will not know how much PD there is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kept 43 rows of data\n",
      "kept 3 rows\n",
      "using stored FE for beta_i_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_i_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_r_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_f_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "WARNING: all-cause mortality data not found, using m_all = .01\n",
      "using stored FE for beta_X_x_cv_ascertainment x_cv_ascertainment {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_diagnostic_criteria x_cv_diagnostic_criteria {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_cv_representative x_cv_representative {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n",
      "using stored FE for beta_X_x_sex x_sex {'mu': 0, 'dist': 'Normal', 'sigma': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "model = dismod_mr.load('pd_sim_data/')\n",
    "model.keep(areas=['GBR'], sexes=['female', 'total'])\n",
    "\n",
    "model.input_data = model.input_data[model.input_data.data_type == 'smr']\n",
    "print('kept %d rows' % len(model.input_data.index))\n",
    "\n",
    "model.setup_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting submodels\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "fitting all stochs\n",
      "\n",
      "finding step covariances\n",
      ". . . . . . . . . . . . . . . . . . . . . . . . . \n",
      "sampling from posterior distribution\n",
      "\n",
      "CPU times: user 13min 51s, sys: 3.42 s, total: 13min 55s\n",
      "Wall time: 13min 53s\n"
     ]
    }
   ],
   "source": [
    "%time model.fit(iter=iter, burn=burn, thin=thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kySQXn5IGX+sQhGiEbCoVozITRwBb1SAjuMFBeOopWLo0bqC+b+7LZAhnzsRbsGB8XpLDqCFJAEnjvZyTtMm5KHJeBjfJFTXtmfiBeHpZZCbSSdJd2bYv1ohG7ksb/Ph+TCw2wdi/cq+Qk3hd2sjnY2knn4/PS9lK1ROU1E2beTgEAeRyHQ2+HOE4TAjaIZukDcYmlGSDF9uM7QKN78c6aZFmpOHaqFYNyaxZA7++OT7/l2+D004z+VQqZuQXjSydSm1qIM2FvTaZMaESE8hARua4yDn7ep1k4XkEInGIikuM73FF0NGkTB1nZNJF0ogW9S6Y8yKhWK75JMsWFXCyPIHtDCBSi5CP78PAAOzfb7b+fujbBwP7YWAQnnssnH++uefBh+Bzn4cbbzBkk8mA59Hd3U0ul2v9R0RwhOMw7khrxPa1pKrC9jQTorG9eepmakuDLJfrJRN7hCgNWCSeb38bbvnd8Ir29ZkGKQ3cIp003bvDxCONYAQ2gQjsQY0QUalUMufCKMJEUsqICzPfUjIumXT+IuXYdkH5loRMhEjstEIM8o2JVGF7jtnqM1FpFQom30c3wi23mPO9vRGpDMBg9C3/bK28EHjNxY1f5hmnw6poTbX+fti1ywzMZs6s2YWGtGbevHmN80jAEY7DuKIZ2STJQ9JUq9Ua4diePnXp0+wylUpMLHZjLZXiBvz2v66vxLnnwEv+JFYZyIxrqe/QEKSoYhwmDmkkY/8/aXYa2+5gO50ElUqdpKDF5ifnZMBie3eZAuNjkXK0Bhn921JJGMZqLskzDM03Z+dhO7boyIusVIJHH4V774N774UnnzJ5vOH1cOmlJv1dd8HP/rPxC9u2zdQrzXOtkIfZs2HWbDh1hWlLngfLT4D3/E38LInJou3CEY7DuKER2dj2l6RUI146yTA1gNGTS4MslcyILgxjwhGVmkg01apppEuXmtGg7ZVz5hlw0UXmXiEZ6TikDFF/7N+Pt2TJGL8th3YgUYyTM95lcBIEQc1t17bF1AYrMhhJg0gp9r4cZ7Mx2UQG/TrnE1v6sNVeIq1oHX+vYK4J2YldRaQeOb7s9Y1fxLbtpg0ALFkMJ50IM2bAnIg8Zs0y+3Pn1UtSN303fh6pt02e8lzZLJz/vFgN53nmXBTxoF04wnEYFzQjG3uEKcey6FPNnRmLmERq8X3TyGQ0WK2aRisNVzqTXbvgC1+Eff2m0KWL4c1vNtff8+7Y66avr35EKaNb2yuoUIByedjcA6XURcCXAA/4htb6s4nrBeDbwFnAXuBSrfXm6NqHgMuBAHiP1voXzfJUSh0DrAEWAPcAb9RaVxqVoZTqAX4EnA18S2v97pH9i5MLQRAwODhYm5NSN09LVKviyWV/dzaJJN1/5dsRw7js2zYSIY5MJs4rk6mXhNLIp1KpV6VJpy3f3+OPwx9uhw0bYMdOk+5r/1qvZhNkMnD6aXDWWUYSyedh716T77Jl8N7/M1wFKN/2/v3mO046HnieUZdJ/raDgkh1dp6RWs0RjsOkQiOVR9JeYxtrbSeBuol1Qixio5H5M8ViPAIViWfTJvjeTbC3t75Cz+42o7QDB+o7I1FtiMpDvIUymXjL5SDq5ARKKQ/4CnAhsA24Wym1Vmv9sFXq5UCf1vo4pdRlwFXApUqpk4HLgFOAo4BfK6VOiO5plOdVwNVa6zVKqa9FeX+1URlACfgosCLaDgkMDg4y2NcHW7eaE9JByn9YKBj1kBjnbbUY1Hfi5XK9azDEAw571D9jRjzil++xUIhVsHbna0sJ0lHbbvjZLKxbbwZDjfD447Bkibn3//wNHH44dHebuotXJZjvPUlKUp+urvrvWL5viJ9B2sHgYFznXC4mRlEhSnmSplQaFumgGRzhOIwpGpFNGtGIesT3/Vq0gNroqVKJycb3jdqrXI4lHDDnpJF99qrhRPO88+C0U02avj6Tp3RSIjWZCpq8u7riOTiSNp+PVRcxzgE2aa2fBFBKrQFWAzbhrAY+Ee3/CLhGGd/Y1cAarXUZeEoptSnKj7Q8lVKPABcAol+5Icr3q43K0FofAG5TSh3X5K+aUgiCgME9e2DHDmMYBzNyF8kjDM1vNmu2ri5zXKnEkoXtBZYkBxm4iLeYdLq7dsWjfZFOCoU4D5FWpPxi0RDi+nsMeWx5Gk4+Cd7/PnNtX9/wh1t+Apx4Iqw4xRCm2JuOPDJWDUdzdGruzCJ9+H49+YnqTiQw+zu3IfUV4pHBnKj6hKzsspSCfL4jj80xIZxJol54P/CXgA/sBt6mtd4yFs/rkI4k2STdne3Z3SLVCBEF8uGLx5k4BBSLMbGIU0CxaLxoBgZg/nyT7vDDY8I5/TRYeYZJ39tr8pMRm3QsQlrSEWWzRgISopMG29cH5TJD/f32oy4CtlrH24BzE6+jlkZr7Sul+oGe6PwdiXsXRftpefYA+7TWfkr6RmXsafwv1UMp9Xbg7QBLJrGdanBw0JDNE0+Y/3vbtnovRLGZ2P+n/If5fL0nmFwXgpk9u14qkY5c5mj19Rn1bH+/+Z76+mDfPmNUnzXLfJNXfKhx5R9+xNQ3k4HnPAdecgGsXBlLY4J8PpZiqtV43oxAImSIDclWg0FMIDYh5HL1TggiwUgbkPLE+86ev5P0tMvlTIibiVzxcxKpFzYAq7TWQ0qpdwCfw6gXHMYBaWRjnxPPIfFAs1VoNWO9EMrgoNkvlWI3Z1Gl+T78/vfwP780Bf3Nu0xDev75cPzxkI/UArt313vXSN2E2KQhysguKcVI+u5uGBwk291tX00LVJVUbDdK0+h8Witulr7dejSF1vpa4FqAVatWTVpXvEEhm8cfN539ffeZC6efbv7Tu9fBM7saZ3Dxa2JC+v4PG6c7bCG86Y3mm/r9bXDHnY3T3nUXLF9er1azkVFw6qlw4nJ49tmY9JYvjyV2MHUqFOo91fL5mCxLpXTpTAZo9kBKiEUktFKpXgITaUhIy1YfQv2z2N6eIvEk5sO1wlhIOJNCvaC1/q1V3h3AG0bzIR0aIxkdAOIZ1UI0ItWI2iwUMpBOv1SKXZztfdt289RTcMON9YU/+6y5Ljr5wQHTmG2DrqgWhGTC0Khj7EabzcYkI+oLGWkGAYUocGGEbcBi6/hoYEfitUiabUqpLDAX6G1xb9r5PcA8pVQ2knLs9I3KOPTwzDPw2GNGulhruQAvXmz++2ZkA/GAotWkxd17jEpMvLOaobfXEKDWcN45oDKwsCfu1G0DvIT0TzoXyDcqEkuhUAupVCdhKBXbZoS4bA83ud+WZGxJLZer98gU+6SQj30fxN+/PYE0akcT7TQwWdQLNi4H/rtRhaeKGmEqIOktlAxBY0s2tdAzIvaLBCOqgoGB2CFA7CjizfODHw0v/E9ebAhH1G0SFdrWOUtDEjuNXBfdt3ij2SE8ZBQpDXC4CuFu4PhIvbsdI6UnfVjXAm8G/ghcAtystdZKqbXA95RSX8RI9ccDd2GklWF5Rvf8NspjTZTnT5uVMbJ/cpJjyxZjTxHJVlCz4ygImzx6f7/5f1sRzhGHm3IKBTjqSFh0VL2qSr4lMN9TX5/Jc+ZM880MDJhv0Z6zsyfScIorcj5v9mfOjPP0fXP+wIH4e+vujr/fri6Tt+3BJhIL1M+xEVuMSD5CgOJMIWoz8VyzQ97YkpGtWpPB2RFH1Ja1bgdjQTiTRb1gClLqDcAq4IUpac0NU0SNMNmRJBubaMSTpVwuG6lGRnwiidi/xaLZSqV6e83QkFGNJcnm2GXGoLp3b71UIioJifVkEwnEE0BFj2/rpmU0J52FkFYuB/39dY0sGjS9G/gFxsZ4vdb6IaXUJ4F1Wuu1wHXAjZHU3oshEKJ0P8BoAHzgXVrrACAtz6jIK4A1SqlPYVTH10XnU8uI8toMzAHySqmLgZcm1NxTC3fdNZxsli42UkY2C8csi1VJ9nwtMOf37DFSRrkMPfPNN9LdPdwVWqRfGZTIoKhQiDtkkYbz+fpOWr4tUYeJ2kq+TbGd2Kqv2bPj9FrHHnCeZ9qE2KJkP+myLHnako58t+IAYS/P0dUVq/LkvOeZfXGGkHcGsfQv58tl8nYcthYYC8KZLOoFlFIvAT4MvDBS0zmMEZLuyyLNyGQ73/dNJy1eX0Iuoi6rVMzx4KAhFpFmdu2CtT+DP7lguNvpwgVGheL7ZmQpBCNG0xkzzK84HYjbp6gF7IYq0ox0HkJA0rDEoBo1wqRnjtb658DPE+c+Zu2XgNemvTut9aeBT6ecH5ZndP5JYlWzfb5ZGcvSzk9F+Nu3w09/Vn/y8IWxJGy7P8t/m8vF6iTbsUA60sHBeJKm7fYsAyNxQ9baSCLFYkweMqCJJkLWvi8hI1Fb2bYSuTfpBTc0FKuAczmzL9/jjBnmOxSnBs+Lw9nI/WKfEQnElt5nzIjJwrYBSRohKSE4eV9C2HKfPV1g3rwJj6U2KdQLSqmVwL8BF2mtnx2D53SIkAznLmoz2YoSnVlIRewxstBZpWJGkSLVDA6aiAC33R4X8sN/h//1AtMATz0lJpfdu2O7C9Q35lKp3ohqhxKxo/nOnFmvZhAngqGheMRYLpsGFpFmJ2uAOIwu9n3ow/UnMsCzliOe6D000B2N0vftBy+6ICqoIDBqN425FrSh3FCYb0A62Ww2jhggpFAqxaotGQDZBCR1sMlHJBCbJGypSKTtMDRqNvtblnk5M2eadiHRNcT5QJ7VDtN04ED07jIxmQoZyWDMttnk8/VqZZGE9u5t/c4sjHqrmUTqhc8Ds4AfRqHAn9Zav2a0n3e6w17DIxll94BMrBSiEcllaCjWCUtwQbl2223w0CPphW3fHjdgcRywg2jKaNaWTETHLioCGf2K+kPcP4eG4gYl0aNtaUpUF1E6RzgTh/lX/SPPrDrHdP4aSDpJ2bxRtBQbQihBSuDVdshG8tZAOfJ2lF/BkDgjeLEhXjwu7cgEQghJEormtqC1IZKkF5qor2xCk7xFupJy5Zx4fiaXKbDtPGKn8Txzr+SfJCIp37JfJcMKNcOYtJpJol54SccVd+gINQ+zMI57JmFpAlGJHTgQd+jFYizh9Pebc3K9WISbvj+8kLmz45Fbb69pePZkT6jvLFS0SSdkW/8Gh8xxSKy3FmOpeaB6I7CtdpNGF43sOllW1+HgoLWmuPZndL/6VahMBu/II429ZsvW1jdPFKoBEAwnJBuVFOLzIu8z8SLL5eqXO8/lYmKxbZNivykW49U5ba/MbDZWrdmRA8ROIyo8cd4R7zdbM2BPhA1DmDMHKhUXacBhbGFHCvB9n2q1SqVSMdKNGFX374/nFshkTZtkikV4ajMMHYgblw1PxRPhensjG0obo1AZgQqSo1859kNDPpmMGQWLusX3TZqsNTMb4vpFRHqoOn9NNuhikb73vZ/iz/4T/7HHmPO3HzQX9u+f2IqNFQINB4pA0QycctlY6hG1nUjh4vRiOzl4npHOxe4CsV1SiAdi+6bYi2wHBCkr6ZggkpqUFUV4KLsVPx3GCknVmS3VUC6bSXgizYgkIwbZ/fuNcf+H/16f6bHLDAnls2bUV4hGWvvGuFMJsSaAJgjEDxnOVkDJSG2dTHZzGBmCXbvY+7bLqd57H2rWLPJnnRVf7OtvfKONbCb6L6cgNLEU1EhSErViU1Riu1amErv/i40I6r3b5Fh+bScDiOejRfcU6idBN4UjHIe2Ie7NEg4+DEPzOzRkjIflchwUc2DAkExfnyGc/fuHexYJtm6NVBARmqkhJgqikgAolTpyBXXoHJUHH6T3LW8j2LkTb/Fiem74JrnlyzvPSMhGpIWq30YH3TmOuON2sosXt06YAn/rVnadd/74lRM0b18jKeOss+zRQGM4wnFoCYltVi6Xa4Tj+z5BfxRPqlg0hCPeZoODRtKRpWt/8avmBdhkM9GYMYOuF/4vCs97HrkVp5BdspRMzwJQih3LjjVpovfgMDYo/s//0Pfu96CLRfJnn82C676O19MDgC6VUF1dLNreuf0m2bGPJklkFy9m+6KR5dXJs4xHOSMpo7q7RWQHybujXB2mHcIwpFQq1RFOaEstfX1GhbZ/v5Fsdu0y+0JArcKBTBQyGfKrVlF48YvIn+hc/UYAACAASURBVLmS7DHH4C1ciFaKYPNm/I2PUf7D7Ry4/ltUNz3OYT9bS+aIIwh37TLecg5jAh2GDP7btehike5LLmH+5z6LssIIqa6uUetwx4skHGI4wnFoCCGYSqVCuVwm7O2N3Zj37TPSzYEDcbTcgQH4jRXCbrLoz+fOpfvCCym84PnkVqwge/QimDmTcOcz+I88QnXDvQyt+T7VRx/F3/TE8HVRAH/TJgoveiHF7/8AogW/HEYfKpNhwdevpfiz/2TmW96Msl13HaY8HOE4DIPv+5RKJSqVCqVSCb1vnwkF0ttriGXfPiPh7NljyGbvXthwX0pG498pZxYupPuVryT/gueTP/kkvOc8B60U/uOPU33oIYZuuonqww9TffgRdH+bhmfAf+xxCmefbQinr8/NwxlFBHv3cuC665n9gfejPA9v4UJmvfUtE12t1pg7l0xXF6rbuCd7Rx0JuTwqm4VczvzmcygvC1kP5XlQ28+ClzFpgLmf/QwqG03+zGXNeS/6te8F5lzxd7W4aSrrQcbM5VGRl5rKRmm96D5JBzznwftR9nIG0a+y1/PBqBuVtbSDyniQ9Wp5DlzzFQa+eHXHr8y1GocawjCkWCxSLpcpDg0ZYtm508zmF5XZ7t1m27fPBMp8cvPEVXj2bLpf9Uq6XvRC8itW4B15JDoI8R95hMoDDzBw881UH3iQ6saNqVJLI3hHHkn2xOXkTjiB7PITyB1/AtkTl1O97/5aGjfyHh1UN25k75vfSrB1K+RyzHnfezvPJJOh64IXk1uxIu7ocznT2VrHAEfcfafpPD3jddXz/ZtMR5/1zG8ua8ggl0XlcqhoEmTtWi4HuRw6CDhq/d1o30dXfYJdu1j4kx9DtWoC0spvpYoOfPCj+IGyHwbm1/cZ+vGPyXTPiNKZCBmyjLod3FYHAZU77qg/5wcQmvA7Ep9QB6Epx4pZqIOQweu/CUEUMFfKr6ublOPX6iZRD6S++D6z3vHXqBE6zTjCcTAT64pFKpUKBwYGjNSyfbshlr4+I9ns2mUkmX37zLm+PuMiPF7IZilc8GK6L7yQ/Fln4i1eDNks/saNVO+9j4FrvkL1vvsNubS5AqGaM4fcSSeSO+kkcieeaEhm+XLUzJmE+/cTbN9O9eFHOPD9NZRu/T2hLGVcKnW06JRDOko3/5bed7wTPThI7vTTmPm6y1rflIL5V3+B7IknUfr1r0G8Ju2OP1r6ovy730HVR/vVqEOtph8HvvmtVtFRCCXtVw15SODNaTwPS1cqqA5coW04wpnmqFarlEol9vf2GoLZscNINf39Zs2RZ54xkkx/P+x4ZtzqlTvrLLpf8XIK555D9thjYdYsgs1bqN53Hwe++z0qG+6l+vBDUGovJqu3ZAm5FaeQO/lkcqecTO7kk8kcdRQMDRHs2EH10Y0Uf7qW3ptvJtzydPPMyuWOAhY61ENrzYHrrqf/7z8JYUj3q1/FvKu/SGYEnZh39NEUXvQinjn3vLa/BYeDhMSGGwEc4UxTyFyavt5eePppI9E8/XSsMtu2LVaptRtnaoTILF7MjItXG6P+CcvJLOwh3L2byn33Ufrlr6jcey+V++5v2+aSPfZYcqedSu7UU8mfeiq5FaegZs0i3NuL/+QTVO65h8Gvf4PKHXfWL+nbLpRyEs4IoatV9n3kYwx95zsAzH7/+5j9vvcau8IIkF1+glF1jjfZRAE3jYotUrlls6i82HAsNZyo7CL7DZ5n2UQi+4pnbDFif1FephaGpmaHsaJQ12w7mXgZbTln8olsRJGtqGYP8jKRrSgT5V1vU0qz1xDZj1Quqk8ux4EbbhjRa3OEM80g6rO+3bth82azkNUTTxiSefppQzJbt46dwX/WLLpf+Qq6Lngx+VNPNXaXcpnq/Q9QufdeDtzwbaob7iXYubOt7LzFi8mffjq5M043vytOQXV3E+7eTXXjRkq3/o6+K64keOqp0XuGctkRzkHAf/JJKBSYf/UXmLF69UHl5fX0ECQiFs+58or6jj7Z8WcTJFEjiDTbT950tNkcyrLhKM+LwxtpjcpkCHbuRFcqkdrNR1crsYrOspfUbCuRTaf7Va/Cf/ppCENjfwkDY2sJfIiOxW5TOPdc9n/u80ZtWLMFGVtL0t5TKzOy6fR865v0f/ozsZpQ6lipoqsVdNUEAl34nW/zzHnnD6unjmw4WoKBjgCOcKYJakSza5chmA0bjPps2zYjxezYMSYz/PP/3wvofvnLyZ+9iuzSpZDL4T/2GNV7NjDwpS9T2XAvvizL2wKZ+fPJrTyD/Omnkz/jDHIrzyAzbx7h7j1UNz5K6ZZb6fvbvyXYvGXUn6MORae6GSlULkfPtV/D37KF/BlnHHR+mQULCHtjwpl5+eXM/pt30//JT8W2mMg2M//qLzB43fWGFMoV5rz3Pey57HWmo/WrpkMV2499XK2aY8uGs2jrFnYcHa8OvGj7VhPBegRYtP1V7Hre89tMu5WBL315ROUADP7rV9tKF4i9cpThCGcaoFqt8uy2bfDww3D33fDkk/DUU8atef/gqJWTWbqEGatXU3jBC8gtPwE1fz7hzp1U7tlA8Yc/MnaXBx5Ay3ryTaC6usiddqohljMMwWQWL0b39+M/8QSVO+6i/5++gP/AA6NW/06QXIDNoTHKf7idA9/5DvP/5cuobJbM/Pnk588flbwzPT2Ee3trx17PAvZ/7vMM/tu/DUs7/+ov0P+xj9eO57z3PZR/f9uo1MOhPTjCOYTh+z67tmyBO+6A2283RLNjB+zpbX1zK3R10f3yl9N14UvIn34a3lFHoSsVqvfdT2XDBg5cdx2VezYQ7t7dOq9Mhuzy5eRXnkH+DLN5JxwPlQr+li1U7tnAvg9/lPKtt47M5jIGcCq19nDgO99l34c/Ar5P4fzzmfnGN4xq/pmeHvxNm+qOK/ff3+QOh4mEI5xDEGEYsnPbNvjFL+CWW2DTJtiWXOW7M+RWnk73q19D4bzzyB57DHR3Ezz+OJUN9zJwzVeobNiA/9jjbRGCt2gR+ZUrya2MVGOnnYbyPIKdO6nc/wAD136d4s9/Hq9QOAnh1sNpDh0E9P/9P3DgOrMe4qx3/DUzXv+6US/H61lQJ+FkEscOkwuOcA4haK155plnCG+8EW69Fe4d4Uhv7lxmXnwxhQteRP6UFWQOP4xw714qGzZQ/PnPqay/h+r996NlmdomULNm1ewt+TNXGtVYTw/h3r1UH3mE0q9/Q++730PYppPAZIGb+NkY4cAAve98F+Wbfwu5HPM++xlmXjayOTatkFlQ7zSQWbiQsMNljx3GD45wDgFordm9ezfVr30NbrwxXua2TeTPXkX3a15D/rxzyS5bBp6H/9DDVO65h/7/+DGVeza0Z0QU1diZK822ciXec58LQ0P4Tz5J+c67GPjyNVQ3bBjZgzpMegR79rDnzy/F3/gYmfnzWfCNaymcd96YlZdZ2FPnNJBZUH88XvC3bh1xQE+/AwP9eJQzkjJyq1a1lc4RzhTHM888Q/AP/wA/WdveDfk83a9+lfEcO/00EwF5924q6+8xhv17NlB54IHhK3CmIHPE4eTPPJP8SkMuudNPQ+Vy+Nt3ULl3AwP//GWKv/ylWYjNYVrAW7iQhTd9jz2Xvo6eb11vBjCjCF0qDesMk04DR/zu1tR7kx3uaHbeaevZLNq+tS4adfJ4JBiPcjot46htT7N+/fr17eTtCGeKYvsvfwlvvbx1wrlzmfnaP6PrwpeSO+Vk1Jw5BI89Rvmuu+n/zD9SWbe+Pemlq0D+tNMMuZx5JrmVK8k85wh0b6+Z73LLrfS+932ELnT/IYPq/fePqONatH0rh639CZk5c0a9TnXLE8ydy1H3rEMPRp6W2SzMnAnQVr3HiyQcYjjCScFU/sAy8+Yx43WX0fXSC8mdeBIU8lQfeJDKnXcyeO3Xqaxb19aMfW/ZUiO9nHmmWS/mpJOgWsXfvJnKXXcz+N73Urn9j+PwRA5TETtPOmXMy8gdeyyB7TCwYAG6rw8OP3zMy3YYGRzhTHXMmsXM115C9yteQW7FKVAoUL3vPsq3/5GBL1xtCKaFSkt1dZm5LmedRX7VWeTPPJPM/PkEu3ZRfeBBDnzvewz99GeTdzG18YACMsqE+Rhh4EKH0UV22bJ6+03PAsI9e/Ec4YwrOnGgGRPCUUpdBHwJ8IBvaK0/m7heAL4NnAXsBS7VWm+Orn0IuBwIgPdorX/RLE+l1DHAGmABcA/wRq11pVkZ7SKzdAnegp6On3+sobIe3ZdcQvdFL0PNm4f/4IOUb/sDA9dcQ+Wuu9HFYtP7M4cdRv7sVRTOPttEADjlFCO9PPEk5dtvZ/8X/3nCJlSOOTJAPt860vXRRxkVDZg1QTzPxM/KZhsSzmT/7huV0Ra6uvAWLGg7eW7lyrbTjhSFF7+wziPNW9BD0LsXF1Z18mLUCUcp5QFfAS4EtgF3K6XWaq0ftpJdDvRprY9TSl0GXAVcqpQ6GbgMOAU4Cvi1UuqE6J5GeV4FXK21XqOU+lqU91cbldHJsyy8/nq8Iw43cYsmGYLNW+j/6Mco3fq7lioyb8kSCueeQ/68cymccy6ZpUuM7eXBBznwgx8y9OO/mNrSy4wuQyL79rdOe8YZhjTuWtc83Ykn1hajqpFNJmO2lLVAJvt336gMrXVbIRMKzzuPnq9fS9Dbeo6Lv307C/6tvRAqB4viv/9HbT+zsKezOThdeRMnrNrGK+gumP+9v0U78RTMm2fim7XC3NkmXbncOnbhwojs2520vXBB67Tz5pjvuXdfe3mmldHhfLSxkHDOATZprZ8EUEqtAVYDdsNbDXwi2v8RcI0yctlqYI3Wugw8pZTaFOVHWp5KqUeAC4DXR2luiPL9aqMytO5sIYv9n/s8B759Yye3TDi8pUsonH8+hec9j/x55+E95wiCnc9QWb+efZ/6NOVf/WrSzNhviHwW5kQN4tk9zdOuWmU+/C1bmi8I95IL4LDDDHk0I5w5s+HYY+MVEYV4bAIajkn93Tcpo21DXPmPd7D3jW9qN/m4Q+Z3tY3TT4+iNGfgttubpz3rLPP/3/YHaNaDHHUUPPe58bfSDM+P4qft39+6/BNPNL+t0gmOPro14Rx3nHn2VoOvwxpoeZ773PbqYmEsCGcRYLs9bQPObZRGa+0rpfqBnuj8HYl7F0X7aXn2APu01n5K+kZlDOu9lFJvB94OsGTJkuTlSY/Mc46g8PwXUHj++RSefz7ekUcSbN9B+Y476PvAB6hMpnhR8+bArFlmMTc7CGZ3l1mVsbvLXD/mmFrYdYaGoFyBfA56esx1MKPDfB4KBZPupJNigpAtSRpy/HcfrCcSW4Kxj8OwXcKZ7N99szJqsNvCaVNszZ/s4sUEz1hrNr3u0vgbgvrf5H9/8smNA8i+/jIjtQAsXmziEQ4MwFDRrIIbWIO3s8+BBfPjspph0SJTZk9PcyI5exWcEAm8g4PtTeg++WSzaGKlGj+n1ElrWLLEtJcgMAO7u+8213y//j0oZcg2DUce2d5zWhgLwkmj9uQ/2ShNo/NpT9Usfbv1MCe1vha4FmDVqlWanbviis6di3fUUWm3TSwyGfLnnM2sv/orsiedSLhrF5W77mbfFVdSviV9HsKoIxvZNnI50+l3d8OMGeY3mzVbV6Tu6uqKG78Xr+FBJmPSR+uL1CBpkuQhH7iMTCU/myzsDgWG36tUTBpp6ezGmRypZjLmWYZjsn/3bbUHuy2cns/XXVeFwuRsC0DutFPp/rM/Y8+fXcLc//shc3Lx4voO0R48QNyxtpJGjjkm/iZ6eupH9mkdbrP8/v7j6dqFv/94XEe7vslyFi+G1atNGc2UNccfbzYw6SQve1/yWLx4OKkEQeM2IDjppMblN8BYEM42wPYrPhpIBvKSNNuUUllgLtDb4t6083uAeUqpbDTas9M3KqNthAP7mfPBDzDngx/o5LZxQ9jby+D132Tw5a8YXRWZp2D2bNMpz5xpJIqZM80WrfFeI5NCpNuOFouqdf42Wci5JNHI+XzebGIrsTcYThhyr2xyLpcbLsnYBn9BIzKyy5RrycamdaNR3WT/7tupX0Pochlv2VIW/uQ/WqbNLlpkwviPE1Q27saOuOVms/NPnzPkYA8iJJ1Sw8nHaj/DJoMuXx7fJ2mT30WhUJ8m+i6H5bVihenMtY47fFvy8Lx0qboFUssRZDIxOdmDujBM7zekPomI6MPKWLmy7foJxoJw7gaOj7xotmMMla9PpFkLvBmjP74EuFlrrZVSa4HvKaW+iDFsHg/chRmdDcszuue3UR5rojx/2qyMTh5kz8X/f0cPPukxowsWLjRkMnu2kSzmzjUidS5nCMXzYmlENiEUuxPP5809NnnIsU0YuVy90T2frycPQXI0ZXX2ttul53koperOZTKZ2nEyinOjqM5peSTLSjsGGi0vPam/+yZltIXK7X9k1znthahZtH0rO5Ye027WY4LMursa/vdBEDT8n5+xVXIRvFOazylq1K3sjOIDynWlFNljjklNb9fHvt6sy5Ln27VrV935IAhQVoSHujwa7afBqtP2bduGX1/c+XxF1WEf3F6mSr0C+GeMK+f1WutPK6U+CazTWq9VSnUBNwIrMaOvyyzD6IeBtwE+8F6t9X83yjM6fyyxe+gG4A1a63KzMlrUfTewJeXSQlLsP9ME0/nZofHzL9VaHyYHk/27b1RGIyzIZPTibOdj0txpp7Ud6mSKYjq3h7baQiOMCeEcilBKrdNatxeh7hDDdH52cM+fxHR/H9P5+Q/22d0qUg4ODg4O4wJHOA4ODg4O4wJHOO3j2omuwARiOj87uOdPYrq/j+n8/Af17M6G4+Dg4OAwLnASjoODg4PDuMARjoODg4PDuMARTgsopS5SSm1USm1SSl050fUZDyilNiulHlBK3auUWhedW6CU+pVS6vHod/5E13M0oJS6Xin1rFLqQetc6rMqgy9H38L9SqkzJ67mE4Pp1h6mU1uAsW8PjnCaQMUh518OnAy8Tpkw79MBL9Zan2H53F8J/EZrfTzwm+j4UMC3gIsS5xo968sxM/SPxwS4HJ8Y/JME07g9TJe2AGPcHhzhNEct5LzWuoKZ2b16gus0UViNCYNP9HvxBNZl1KC1/h3DY+w1etbVwLe1wR2YeGZHjk9NJwVcezA4JNsCjH17cITTHGkh54eFdD8EoYFfKqXWR+HqAY7QWu8EiH4P5XV8Gz3rdP0eBNPx+ad7W4BRbA9jssT0IYS2lzg4xPB8rfUOpdThwK+UUo9OdIUmCabr9yCYjs/v2kJjdPw9OAmnOQ4qpPtUhdZ6R/T7LPBjjCpll4jL0e+zE1fDMUejZ52W34OFaff8ri0Ao9geHOE0Ry3kvFIqjwkPv3aC6zSmUErNVErNln3gpcCDxGHvoT4c/qGIRs+6FnhT5J1zHtAvqoZpgmnVHlxbqGH02oPW2m1NNuAVwGPAE8CHJ7o+4/C8xwL3RdtD8syYZYp/Azwe/S6Y6LqO0vPeBOwEqpgR2+WNnhWjQvhK9C08AKya6PpPwPuaNu1hurWF6NnGtD240DYODg4ODuMCp1JzcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwcBgXOMJxcHBwaBNKqeVKqQ1KqQGl1Hsmuj5TDW7FTwcHB4f28XfALVrrlRNdkakIJ+E4tA2llBugOEx3LMWsjeMwAjjCcWgKpdRmpdQVSqn7gQOOdBymK5RSNwMvBq5RSg0qpU6Y6DpNNbgF2ByaQim1GdgHvBrYo7UuTmyNHBwmDkqpW4DvaK2/MdF1mYpwo1WHdvBlrfXWia6Eg4PD1IZTqTm0A0c2Dg4OBw1HOA7twOldHRwcDhqOcBwcHBwcxgWOcBwcHBwcxgXOS83BwcHBYVzgJBwHBwcHh3GBIxwHBwcHh3GBIxwHBwcHh3GBIxwHBwcHh3GBizSQwMKFC/WyZcsmuhoOE4R2nWiUUqxfv36P1vqwMa7ShCHZFrTWde9HKVX3eygg7f8/lJ5vrNBuW3CEk8CyZctYt27dRFfDYYJQLLYXKq67uxul1JYxrs6EYtmyZdx5551Uq1W01gRBAEAYhoDpiHO5HLlcjkxm/JUlNjnYdRoJEQZBQBAEwwhHKUUmkyGbzY6IeNIIerQxlmVI3mnvxS6r3bbgCMfBwSEVWmuq1SphGBIEQd2vUopsNlsjoXw+D4yPNGBLWmEYDpO8MpkMmUymdi7RMabmJ2ST7FjlnDxvJ3W0f5VSqfU5GKRJY6NdRqtyOi3LEY6Dg4VqtdpWuu7u7jGuyeSAjPzDMMT3/RoJSQcqnbGQkByPRYeX7PiEAJPXhECEILTWNQksrfP0fb8mISWlHKUUnufh+z6e57V8rkZ1HQsiaFWP0SirmYpZyuhkLqcjHAcHh1RorWudehAEtY5ZhyEaqGI6UCHpfD4/jIiSaKcTbNRZJ8/Z5JAsU0hQiEYIMQ2ST9pgQ54nm80ShiGe57WsdzM11GgRz3hM2BcStt9dst6dEpsjHAcHC77vT3QVJg2ETGzVGkEAYQhaEwKVSqUm0YitQ2BLFva5RsdpnVkatNY1acuWcgSiUpN9+760MsIwrJGNqOgEItUI6aYRTjOVU5pKLe1ZxwIHI+W0Q54jQVuWPqXURUqpjUqpTUqpK1OuF5RS34+u36mUWmZd+1B0fqNS6mWt8lRKHRPl8XiUZ75ZGUqpHqXUb6MV+K5J1OsspdQD0T1fVs7dxKEFZKTcapsOECmiXC4TBgFUKlAswtAQlEpQqdQ6fJsEbIRhWNeJN3uPcs6+J6kqk3Ikja3uk/MijUleyTJsCJGAGWxUq1UqlUptq1artTRpTgWNnqFR3ZP7I0G7ZR5M/mn/l32c9v+0g5aEo5TygK8ALwdOBl6nlDo5kexyoE9rfRxwNXBVdO/JwGXAKcBFwL8qpbwWeV4FXK21Ph7oi/JuWAZQAj4KfDCl+l8F3g4cH20XtXpeh+mNUqnU1jZdUDO+VyrxFobg+4Z0gqDWSdtqN0ErErHR6JrcK8Rik43v+7Vf3/epVCq1NMm6pJVj51OtVvGrVcIgqG2VSsUQrqVesvNp9ByN0EhiGAkaDYSSRNdpnu2UZx+nveNGaEfCOQfYpLV+UmtdAdYAqxNpVgM3RPs/Av4kkiZWA2u01mWt9VPApii/1Dyjey6I8iDK8+JmZWitD2itb8MQTw1KqSOBOVrrP2rzhr5t5eXgkI4gaG+bTgiCmkRDGJrfIDCkc+AAOur0y+VyrXNOqqag8ci5EdGkXZd8g4gMqtXqMCnEvpYkOjsvW2qpSXHDK0FoEZot0TWqb6P6t1JTdYJWUuJoSzqNrnXqINIO4SyifsXHbdG51DRaax/oB3qa3NvofA+wL8ojWVajMprVe1uLegOglHq7UmqdUmrd7t27m2Tp4DC94Pu+IRat68lWpJxI0gnK5eh0WOvoW3VWzTrkNGlIVF+i7rIJxpaC7HlDolpLEoGkkw2xT8kWhvEWlRlE0lwnUkDadTtdp2j3npFIOY0IsVVdO3mOdggnjb6SJTRKM1rn261HO3UaflLra7XWq7TWqw477JCdOO7QDrRub5sG0NrYS6TTJQji55d9UbP5PtVisU7iEAJolHfa6FzuS16T85VKpZauJmnY5Bc5NCRVbzDcnlRTpwnh2M9WrQ4jHiGbRnXv5L0erATSzv2d1il53EhaE4hk08mk33a81LYBi63jo4EdDdJsU0plgblAb4t7087vAeYppbKRFGOnb1RGs3of3aLeDg4OTeB5HkEmA5kMeJ7piG0oZTr6chmUwq9UyGQyw+bBAA0nY9ppGk3MtMmozistqQYLQ1OnqA42gdmeYqIi830/JpZqtX5QEeWD54HnxZ56kOoi3ciOUv+6VF1623W7FWyiTJ5LK0OeObnfKN+0c8lnOti5Vu086d3A8ZH3WB7jBLA2kWYt8OZo/xLg5shusha4LPIwOwZjuL+rUZ7RPb+N8iDK86ctykiF1nonMKCUOi+yDb3JysvBIR22cbzZNp2QycSdr9b10oRSsYQR2XakI7fVT0IYNqTDbaV+E6lJHARq94hUUq2a8uU3IhEd1SOpchPSqlarBCLJVKvx/UJA9vkoP9spQeqd9tvIdjSaKrVWartmaRpdT6uXLamORFVno6WEo7X2lVLvBn4BeMD1WuuHlFKfBNZprdcC1wE3KqU2YaSOy6J7H1JK/QB4GPCBd2mtA4C0PKMirwDWKKU+BWyI8qZRGVFem4E5QF4pdTHwUq31w8A7gG8B3cB/R5uDQ2NEtgiHeJZ9EI3wa6Tz8/8GvwrHHQ9Ll8DcubETQRAQVquUiUfaMpdFJmM2gi3lSMdtd+5ghbIRwrPUaNHNpo5BAPk8frVKkMvh+35dPDTf982+1EfIxu5E7RF8lK8QmOd5DaWTdjrlpKTTrrTQSoJK5tlMyunEFiUDBvkmbKm1E7Q18VNr/XPg54lzH7P2S8BrG9z7aeDT7eQZnX8S48WWPN+sjGUNzq8DVqRdc3BIxTSxz7QDpRT5fJ6KuIGLzebr30i/4bnHwsteCpdeSqg1Ja3xosmgyY6ykUpGiEYmWwpJ2c4AoZCMLdEkCSeXq6n/hGykXNttuiYhCfHYhKhUrDIEyGbxKxX8QqEWO84mnTS7R6v3K/e3IpyRSERppNNO3exrtrQmZCP7ycms7cBFGnBwsNFOA5pG84eVUmSyWUIxpgOsOAUefGh44ieehHs2wJ/+KShFuOFeBt71bli4EI47DrVsKbmlS/GWLiG3bBnZY48lkwiHk7TX2MdCRjX1nZCNqMHktNFQXAAAIABJREFUf/E8QxyFgjH2l8u1DjIXSTtBNM8G+7lEUrJVf9lsTDqRmk2iEiTtQq0Ix05rk42dVyMcrCqrnXvTpBqIvQNtCVWkHEc4Dg4Hg3bsM9OIcMB0LiHEz/1/P2RUj6Jq27sXnn4atm2H5ctN55zJwFNPmfR79sCePeg77kDebhGY+7tbUYuOIggCBr/5LcLeXrJLl5JZshi1aBG6p6dGMjWCsLziapNPkxB7jmX4F4mmzrvNUgPWSUx2JxqG5hmzWVNuFCG7XC7XLctgL49gd8S2CrFRmJ1mDhM2Dtbm00jaSUtvv3db0vQ8j0z0PpPP1A4c4Tg42Ii8rVLRJHDjoYi6Ea44DHie6eQzGdMB53LQ0wNHHAHnnGM6ZlFRXfQyePGL4NndsOsZ2LETduyALVtgxw7ChT1xrLY1a+DJpxg29XL+fHjNq+Gd7zDlDQzA5s1w+OGmDhCrvARKmbqWSqa+kXdctVqtRX4ORLqRugqJyeRWiG1BQqyR80AQOUTYqkL7ndUkMeoJRyDeeu2q1NqJbtAOkt5mafVu9Bxyj0ieoqJsFF+uERzhODhE0FrX6/CTEFXLNJJwyuUyobhCZ7OmwxepAEynnsvFZJTJmI5ZKfPb1QXHPRdOPolstFibOBGEYYiSkfLlbyN8dCNs3Wokoy1Pm/z7+owEJdLI+vXw0Y/XV/LI58DixXDE4bB6Ncycaepx220wVIQZMwi6uwlmdFPNROq2ngVGGgtDGByEO+6EakQ2oYbnHAHLlpnnyuXM83R31+ohHa+M9m31Wlr8OKAmKdhSUFIl14mtpV3YZbVrK2r0HHK/nE8j3WZwhOPgECFIC1uT1kCnCeForeNIA7ZHGMQTIsW+IVKFuFBXKvExQDSfR2tdiyotI+MwDOlavRr9mvolB6p9fUaamTnTSJ7VqgkcmsTOZ8wG8JKXGJID+NKXwW8QhuikE+FfvmzqvnMnfPHq4WnOOtNIVoJCwdQjm60LDpo0oqcFK7XVZ/Zmd+DNJJ2R2EuS9zfzjEvabtqJeyf17gSOcBwcIlRFh98K02TxNYg6FFE5lcuxak08uNKiD+RyZl/UbpGNRHd1ERQKdSPopKtyjeTAEMfy5bG6Kwzh/PPhRz8wddm+3UhCjz9uiGnnTnNfZGvhrLOMI0OhAFnP1CuMSPDEE2NbT6NOc/098LGPwyciiUpUiWFIWKlQKpWYMWNGrfNOzjuC+kmYcmwb4e1rsp8kp2Zox0bTKP/k9aRUYztqiP0m7d5OJoA6wnFwiFAzJLfCNAneqZRZ4yYUozrEUo39rrSOpRxRv+VyhhTCMFZJyZozhUJ0W+wCPcxmIPmUyzHhVKuxjSWTgaOOgiOPhLNX1Rv8ZTLnu94J+bzZZs2K3aXBpJGApAsXwjeviwk0COC/fg4/XQvbd8ROEJJ/pQKRe7AdNkc6aXl3SQkGqEkx0oHbnmp29II0e0oSzaSPNDJLzhtqRT5JLzVZ9dR2Me804oAjHAeHCHVxw4aG4MbvwGsvgQUL6hNOE+cBrTWhHSFaPLZEorEhXl5iXA9D09HLeSEp6YBzuZr3WLIT1NLpixeadPJCJCIB2bHO7HpIPW1pTEhDbEwSRcAUan6r1Vgl+IqXw2ELjSQUzbmpkZ5SUCjUPLik/nbHaxNp3ftMEExy6et2Q92kSVLtuk43UqfZjg7J9YSkXrbkA9Rscu2iM582B4dDGH4Ubp8DB+B7N8Hd6+DvrozPyTadohGIxCCEIc+ezcbRB+zOrVSqlzREyhFykOuRRKK1JvD92qaFCESFV6nU1t2pc3dOuj7b5COEJXX1feMYIHYgkWyE0Oz6FYux6/Xppxv1qXivffwT8MwzNfVeWC7XQt3ImkC2Ciq52R27LQ0l7SBJMmllR5H9tC2JdspKSjdhGJr1gcLhYYicDcfBYYSocxq4407z+7zz0gNETgNoMfrn8/UqK+nY7ZG4SDfiFh0ExnYiDgT2O6tUYhtQsWjOSV7JNXeEDCRPW2UmEoyUL/+f58UqPnFzF+nFmsBZu1ccIuyJoJK/POvnPg9bt8E73w1f/Ccj+ShFNQpWaksttveZ7R1mSzhyzQ5yCsPdjBup0mxSsNV3yfON/ldJL4QhdjOJg1cn3UT/Q2ipz8IwrM1DSsbIawYn4Tg4RKjIaPeee+KTr3xF/doooiqaBlC29CIEYxOOdNxyXpaglmvFopEgRMqxIwPYBnsdzZkpFmOpSMqVdCKdSHohAwmuaSpsrg0NxfUQzzbfNy7dxaLZLxZh//76iaRCivYzSvlvfUv8Yt7/QeNQEF2TKNK2kd1e/VQkoKS9J+kNlmY/aSbdJB0G7PTNpJzkOVH/JdPU5ivJVq3W5iDZz9mJDccRjoODQEa33/meOZ41M7ZDiN1COrtpgFpwS9tzT6QOkTTknUgIGOnkZdQrHbaQiXReQgJCUrLEgZBYuRyrwezoD+JuLQQhqj2RCmzVmm33KZUM4ezfb8qWepRK9WmSJFgum7Jmz4bPfiaux0c/Br/9LVQqtaWopZOuxWmDumWubVKyt6QLsh11Ook0l2r5r+xzSWknmYd5VUFdnmmrmmrbbha9M7terQKyJjE9Wo6DQzvQ2oRhEVx6aTyCnqbLE2S6uuoJVtQ/ErtMCEkM9WKgF9uKdEbSwQsh+b7p+O2Z/lobG5nYWeyOzv4PRE2mrECdnhfXSaSfYjEmLrHbBEFsh5MBhpQHMQnK/14ux2SZzxt1muDT/wg//amxRfk+fkQ+YVTnICIwHYYEvk8YRSmw48TVwvYkSKcR4UC9RJIknSRakU7aBFU5X4s1Z6+IGpG52HNqUbfbhCMcBwcbP/hhvH/MsnrJRrbpCCGdMHJzzmZjQrA7f9lETSUGfTCdt22wt2Oiib1FpB8hIZGQhBikHJG65BjqpR+pq5CNSD39/bFqLTmIEGnGnmMkkpdcz+XgX75kohsAfOe7Ji/bUUHI0SathISQXGMmTbUm1+z9JHnYpGW7mZtXUx8VwIZNeFrrulVa62LN2QMLeaeWxNaI0BrBOQ04OAgqFTjmGDOZ8OyzGnujyVyOaQCtdb3KqlAwHazWsWSRjCognZSo3LLZWCopl82EzkIhtv3kcvVqMNsDTUhG3JqlcxO3ZxmFiyOC7VxgdfI1ErFVZoVCTCilkqmXzBlKulCLxFYqGc+1T3wcfvTv8KY3xhKdkJVNhuYlxk4LEYKE6kv2xWFA5rwkHQBsQrEdFOx8bFdtgT3Hx/5v07zUAktKq/0P8hzRvqgM7Tq2A0c4Dg6CUglOO9VsYPT9MDyUzeDg+NZrglDzfMpmY2eJgYGYgISQ8/lYOlEqVm+JMd/3TRohJfFMEynJ7oztUDqlUhylWdRkQhy25CQEVa2a+pnKm61YjPPI5+ttUOWyOSdzfUQ9KBKUSCsy2rc99oIA/vy1sa3H9+GmNfCWN5vy5T3YsJwttFKEUdQBmQAaJ6sPmCn7YqC31W22d1p9UXGonOQcH3uNIdvuVLcEhEh38h7lnSWI0rZXtQNHOA4ONhoZQG3SmUZqNS3Sg3T4mUxs3Jd1Z4QIRBIQO410XJbbL0qZ46GhWMqx7T+Sn5CAkJaQi5AUxOVKWVKeSFxyn5CCUjHxQfwsQkbi2i0dqxCPuFpHcdTIZuOApUKKH/s4bHoC/vAHE6Otq8vkl8vVq/gk7I/vE1oTRmVhOPvYjkZQ+y+ol0ySZJOcfJqcVGteSTCMtGybTE0qlPcsAwkpJ0qby+XQWruJnw4OnSLYtQv+6h1msmea3cZ2BZ4mUEqZpZxtiUVUZBB39Pb8HFv9ZKvJxI4hHmvlsrGn9PfHHmRi3xkYiKUhkVzszk9IR9RY2WxcLyFBcbMWUikWYy+1Awdil2iRVoXYxOYi+7Ybt3iyybOIVFcuw8UXm3s2b4HXv8E8l91x2+o2+bUki9oEy2hLerPZc13sa0mkuVjbNp0k2STL17YruPyftg3HZFQjnU7IBhzhODgAcOB7N5l1Wu68a/i8m+Q2TebhAGRkqeakxxLUd0y2UVmISew75XJ9TDSZE1OpmM5fDPv79sVkYzsPZDKxekqkIxkIiEQiJCSSkpCJbfCX8sRzTYhPSEC85pI2C9vBQYhM8hTCOeN0+IdPmvsGBuCSP4dnnx3+XuyJp9GxdPgSrSCpXrOPk5Ms06IL2Go4uTd5zo52UCM5IVV7TpKUlxx8RRKY2Jra/p7aTungcIhC+z5D343m3py0vDXhTJNYanWBGcVAD/GvxB0T/b7txmxLACIZCLmIbaS/36Q7cMBcHxoynbWtzpFOTtRkxeIw9U5NtWVLFLKJJFMsmrKFaIIAentjl2gr3A5hGNt3bJIVxwQJtyN1Ei+3k06sd5t+81th06bhHl6Sn0hvlpTRaGltmyjs/8c+b3ujyf8mNpukE4Go7GzCqUmrNinK/25LOxHpZDIZPM8bRoLN4AjHYdqj9JvfEEho+3POGe4um9w6aGBTGVqbOGdA/YRKOY4TmvOFgumkbXWa7V4rHbpICSJpHDhgFloTgioWzTmx0dj2HNsDzo5YIOVIuUKGpdLweThCbjLRU9R8EJORSESSr9TNdnkWicn3Tf2Hhkz06q9+JX43//KV9HdhS4nRNZEYRMpJukrbarCk+i2NsJKEVPtPo3QSSUDrKGCqTTZSX9nKZbjr7ph0o3A+uVyuo3k4zmnAYdrjwLdvNDvnnF0/um6EaRJpAIglFlvCE3uHQAzjYu+x18GRTlvmsIiEoJQ5HhqKJUZ5r9WqIS9JB/XqTCGgbDYegdsTSW01nkzalHIzGZM3xHWQ+7u6zDIGttpQ7EFit5I6in3IVvkNDZnF4hYsgOu+Dl/9Gnzg/bHKznaikDJyuXipbs9DY1ymhThEyhTnATuigC1Z2K7R4hYt9yWjEgD1NiN5fjvEj+UCzYYN8JGPmf0vXQ0rVkAUR60Tl2hwEo7DNIe/eTPlW241ndAll8RG5aGh9E1idE0AlFIXKaU2KqU2KaWuTLleUEp9P7p+p1JqWXR+mVKqqJS6N9q+1nahtupH1ClCEOL6a78P6TyFdGxVTKlk3q9ID6Lukln/Bw7E8ddEArE7Qc+Lz9lzfmzpRtR18j+JVCqTP8V1Wv7LXbviKOBCkEKc0bo3dHfHxCX3iYpQiFacCUTyyufhbz8YzzHauxf+4o1w//31yyvYpBwRSBiGlEqlOlJIrjBqLxFg226Sc3Rs6cie2FmL9SZkY6sTZRsagg9/JCYbgD1762xPWuu2llMQOAnHYVrjwHe+C0D3q19NsZBvzxNtAtyilVIe8BXgQmAbcLdSaq3W+mEr2eVAn9b6OKXUZcBVwKXRtSe01md0UqYW24ipgPmVEbl4rolHmnTQENtZstn6jl5ipolxH2IikfkvQRB30qYSsQpNnDXkupCbPRq3jd7igSbEJ2lkND97dvwMCxYYwhDJStKIXUM64EKhXi0mXnAzZ8bSk9amrpVK7B79L9eY+nzik3DicrjyClOmeNkVi/G9mQwa8CMbiUgrdtwyO+K02GiSnmlCOplo3aGkk0BtYT3bFdqWbj76cXj0UfPOpM7z5tXUm7ZKrl04wnGY1pj5hr8AoPtVr6S4cWN9oMpGmADCAc4BNmmtnwRQSq0BVgM24awGPhHt/wi4RnWiYE+gFmVAOiDJSlRCMgHTtpvIeZnTkssZ47xAricnior6rFAwnXRXl+mEJYyOTN4UwrM7e8nXJgchDIjjp0mkApuUsllDDsWikWSGhmDGDHNN7DrSGdtu2HIM5n5xybbjvIkaD+Cv/wq+eDU88ig8uhHe8jZ465vh1a+ud5cWQstkCKrVmposuVKoPRHUdnuWpRKSwUBt+48E6axJrraEI8/leabO730f/O0HYOXKegcRrQnLZYJ8nkoHsQUd4ThMa2SXLWPuRz5sDh55xHQ4NqTjsI87mFk9ilgEbLWOtwHnNkqjtfaVUv1AT3TtGKXUBmA/8BGt9e/TClFKvR14O8CSRYvMSdsuInYDe26GrPJpz78REqlWTUc+OBiTkB17zIY9kbRYrI9OUCrVh8AR+40d4w3q3aOhPjq0bX8S+0k+H6/jI2XYnnCi/pPnE7uNOEpUqzBnTmxDApg/P37uIIiXuP7YR+G+++GzV5l037wBvnsT/ONn4LjnxqRrvQdfKTKW/SYZlSCpVrPX27GlHDu6QJD0JhTC+eGP4Mc/geu/YY4XzIcbb4glUHtOVvQOyuVy3Xo+reAIx2HaYpgxVYzMgsnlHJAmqSRFrUZpdgJLtNZ7lVJnAT9RSp2itd4/LLHW1wLXAqxcuVLXxTwTe4jEkhMJQwhYpIc0UpY87PVpRH0mNhkhi6Eh02kHQRyqxp5fI8QjRn9rbkiN8MQOJORmuz9DTEqzZsU2J3HlFqcCsT/Za/RItALpgGfMMGSqlJHKJHTPzJnDvdIyGTh1henQf/BD+J9fmOfZsoVaoFjxrhP1YqVCGJFrxvPqYpdJWJyktJOMsSZkJSFsakt4S70eewyuuBJKkbT445/Ay146PIipkKqECNLxsgbtwhGOw7RE9eFH6H3Pe5j1v/83My/9c3PStlnA8BhqgqQUND7YBiy2jo8GdjRIs00plQXmAr3a9FBlAK31eqXUE8AJwLpmBSrpbESdZc9XSc5FEuO3HXRTSEVC4YiUJJM3MxkzF8e25xQKxrbS3x+ruzwvDhoqyxEIYUgnnZRGbAO47ZwQhIaCc159qJv9+2OCEQlNXLSFBPbvN6Ti+6YOoh6EeHCitSEbuVfISwhNVIR//lrTqf/il2ZVWbFR3X8/nHFGPZFGUlUYkYbnefi+X6dmS8ZMSx5H/70Jymmv//OvX4X//K/4f7x4Nbz0wnpJVv5DIcHBwVhdKs/XJtoawo3UOya69qHo/Eal1Mta5amUOibK4/Eoz/xBlLFZKfVA5JnTtHE5TC8cuPFG/EcepXr//fHJvr54FJ+2Bo64+k6M5HM3cHzUPvLAZcDaRJq1/6+9Mw+Tq6rz/ufU2lvSnXSTIE3C0t0RQQiQNmwiIDO8yhZAXiIYQAYHF3jUZ3yeVx2HRUbHUd/RV0fEV99xEBAzypKAD6MQdpEtMQhiSC8JkI1AJ51ea7/n/ePcX99b1VXdVb131/k+Tz1Vde+5Z7lV53zvbz3A1e7nS4HHtdZaKXWQ63SAUupIoAXYNlqDWozf4oYsLs+5sSh+24h4o0kGZnkdOOBJN/v3e5KM3+tLpKhYLHu30N5ec72k0BG7jb9NIQupRxZWSZ0zGIO048mEKZ9Xm7zMDfLUblp7rtv++kRNJ/FEkprHn+k611FByE/UklobVdzqyzxbyrPPwv/6Cqy5CrZt92xR4njhGvxlsze/E0GuJ5rfS00pZbYf8G+ZsPllOPd8j2xqquH7/wYXXuARkjxE9Pd76sP+fk/SccdSSqaBUSWc8XjHKKWOxkyMY4BDgA1KqWXuNYXq/Dbwfa31Wtd981rg9lLb0FqLgvgsrbVvVy2LcofT38/gffcDUH3lGu+EPwBwJFv7NGQacG0yNwC/B4LAz7XWrymlbgU2aq0fBP4DuEsp1QHsx8wLgA8Btyql0kAG+IzWev/wVrKhJN2LPOHKgi/2Gb+tRhZ82boAPDWMLMT+vWKkLHhP8SKtiGG+psZb9P3Sgd947XcekOvEpgSuZJYYrnwEQ0DptFlEw2GPdOrqvDFJBgP/Q0dlpSEdIeNUCmprTT2BgCFHcSSQxKDmR/QkMyFusdcEg9DpPgO88w5c+ym4+CKztXU0OuSooV1JSAcCWaoskXrQGuU6DmSpi/22GseBp5/2zl3zSTjzDHM+FvP6JGQnv7HYasRbzyXCCSUcxucdswpYq7VOANvdibDSLTesTqXUFuDDwBVumV+49d4+hjaeK/IeWJQZYvc/gB4YILLyA4SPOso7MVrQp0g2Y3f8Ghe01g8DD+ccu8n3OQ78zzzX3QfcN6ZGxX6R6z4ri6cszP7ASiHuVMqLVQkEvDgbkRhkYROpUchJVDXJpCGdykpjK0mlPMIRIvLvWSOqNFF5yvtIToXxhGdzkqf3cNi065e8xFAv5ClqwYoK77zW5rucE6KRwFG5T3Iv/QQEcP550NoK3/ymkcgeWGdet9wMp53qEZXblvZ5w6V9+/xorXHAJOIUlVgqBbt3Q329+fzx1UaiX32ZpyYcGPAcBERaFIlVSF/a7+szv0siQSo3EHgEFKMbyOcd01iojNY6DYh3TKFrCx2vBw64deS2VWobYP5qjyilNrneN3mhlLpOKbVRKbXx3XffLVTMYg5Aa02/m1mg+qors0+KnSF3YZVXblqSOQ4twZh+24ZfRSQLmWRiFoIRghJikQzQsohlMkZC6Onx8qtJsK0s3v5dOv0vITLphz/oE7zdPKV/o2WN0Jj6JODXv7meeMKJak/a6uvzVGgy9r4+oyrs6soOLHUcL7DUn/laSFMIXexP7zkYbvuR2dhNcMvX4a67vfsvZCXfxagPQ4Sm/XaaXbvg+hvg8k/AW2959++TV3vbMsiDgV8V6pdYpQ3pp28zvFgJNs1iJJzxeMcUOp6P6EYqP5Y2AE7TWu9WSi0CHlVKva61fnpYYZ9nTmtr67QEWVhMDZIbN5HesoVAfT2V556bfdLvvVMI0+cWPeVw5ElcCAS8RTLXMcBPJrkLld/mI0ZnseGA55UVCpmn5nTak3Bkh1BfDq8hg7VII377iNiBuruztzUYCSlftH8sZqQpf2qeVMqQiewQKpKAqPjE/Rk8pwZxNBAvP38GZr93nd8GJeWiUTj9g0aq+cn/NfaWM8/w7ruUBS9+R/6XuQ9D998PP/h37/szf4C//RtvbOCNUew08oDlJxa/FCNtyu9cVVX0f6oYwhmzd8wo1+Y73gXUKaVCrhTjL19yG1preX9HKfUARtU2jHAsygeDv7kXgKqPr0bletjIE/ZI0Dp7Q7G5DFloRLXk99gSe4Zf1SZP6yKdiCeaqKrEJiAeXlpD0kfela4nmsSxiAF/YMCzh9TVeYulP5ea32lAnsL7+rLrHwn+hVYIwL/RGnjbEjiOZ/8RVZ/jeN5bompLJo0UV1XlxdfIOMS243dSELUbeHnnvvgF047YlRIJ+PJX4bOf8Xam9btRS9937ICv3WikG4BwCG69FZYu8bwN/bYpf+oikbikz/KAJa7pQm5iuypBxVzMzBnyjgF2YQyRV+SUEe+Y58j2jnkQuEcp9T2MQb8FeBEjlQyr073mCbeOtW6d68fShlKqGghorfvcz+cAtxZ9ZyzmJOpuvYXoSSuJnLRy+EmJSBfkBnzKezFPzXMBsgD5PcZEnQPeU7BINf39nkpSynV3e2o2ca3124T8iLlG+95e41oM3pO8LGxi+/HHxoh7tEg64JFcKZBxiK3C71odDJp+pVIgQkRGQ7+rKhPbhrhyRyJG8hG7izyoCMmIalBieiR+R4ijosIzzIu0FwjAHb+ArVtNBoDWFfCPXzVkJON2HLjvfuPuLPjYJcZWk0x6CU5F+vM7boD3wODf+VTsTEJK8vu65BYuYX+oUQlnPN4xbrlfYxwM0sD14j2Wr063yS8Da5VS3wA2u3VTahtKqcXAA66nRgi4R2v9u6LvjMWchKqooOpjl+Q/2dcHqTyLlPCOkE5l5aT0baYhEAiYBVGCG2VRF/WWkI0/jYzEMoldRBYwcQcWm0g8XtiYH0966Wz83m/SpuRp89tp/ButCdkUK92AKStEKAurjFUW3JjrWu2HdvsLXlyPUoYwBwcNUUrskZCu2In8m7/5A2YlAFYISYg/FDJxMp2dsLUNNm6CSy6FL3wezjvXI6slh5r658+HW26CRYtMXySWStSH8nAlZOPfgE5in+JxLxGrjM+fIHXx4uLvMUUGfo7VO8Y9903gm8XU6R7fhufJ5j9eUhtuPcvzlbcoP2jHQQ8MEJg3r3ChVCr/IqhzPkyTl9pUQ4kNQhYfWSDFXiKLst+xQIzPYkwXw7monSTxZzozsveYqN/89g2J5Jd8Y/LULwuy35XXH8BbLIQQZAxae7uODvV7BIeRAV+Wit5e4+nW328kEBm/eOaJ9CDu1XIuEMiO+xEyEqkJ4MZ/MlLOP7tL3g9+aF7r7jdktnw5/Ms3YMkSM54DB7zx+V3V/XYZcerwqxXFK1BsTWLbEtvZokWQStls0RYWuUg8/TT7//7T1Hzm08z/0j/kLyTZhUdDmez4GQgEaGxtNa66JSD99tvsXbPGk27ENpYRx4MiKon7shbAEMEs/ta3CB18cGkDkX7t2MHek08tXEAIyx/IKjaNYlV0A27C0Z4eI2FUVxuiyHUekAVdyFtUWGK78au+qqo8IhBpo7kZ7vg5/PIeeHSDafvh/4a/Odt8Pvzw7H2B/Luviuu4xNeITcevLhY7m6jSIhFPZTpvnvnu1lGKW7QlHIuywMCdd6EHB1EjGfz99puRUFExMZ2a4Ui98gq7GpeMXjAHjbt2eCq2oQ3ZCkiPIyGjvZgP14AfOvjgMfVpqF8+LH7+j4SWjK2uEcmrv99INz09ZnGurPQcTfx2IXn5pQqJM0qnPTtQPO6pNisqDHlUVpr7esXlJkXOf/3ak0CERPwu1GKHA0+C8mcPkMDXaDR7ywexu4kkK1spDA6amJ5YjMCCBUXfN0s4FnMemd17iD+6AUIhqi7/eOGCxT6plYmEMy74E3JWVkKi+KfgLCQSZiEVG84EIrRkyYSRVxbSjlFjOY7nvh2Pm/sQiZiF2+8yLmpByd8WDnvbM2QyXr4yISVxuw6FDFlUVcHfXWO+Hzjg2Z1EQhQJLZn0HqokFkpUhUI6uVmuRaqS4FX5XcVjL5EYytlW1D0f0922sJhFGLjnHnAcKs87l+CiRYULFqtSK2GClS1ksYTiiTwfUhnPa2o2EX3/oEcWYgcBI/VUVGRvTCdG/GhttuDhAAAWc0lEQVTUHBOykdQ5knmhutpzxR4Y8Oru7/ckn74+z04j9Yt9xu8CLQG3/uSnsrupbPsQi2XvfSTqPaU829D8+SZHW5GwhGMxp6FTKUM4QPVVV41cuFg32jIknMCCOsLvf//Q2LU/AwMYdZnve/Xq1WhZ+BIJcHzXDeVBk2sd81mLa6/5PFQWIBwaepIOtbS410l7bt2OA8mUaTeZRKfTJuK+2ADQiUYsYdzCZVO5/fuNim3//uw9ZvwBtmAIorraUyVGo16QbXW1RwSi/vK7RAsxiEqtr8+TasTgPzjoJUMVcpEcdOJBJw4DYseJRg3ZiJQzMODZfkrIvGEJx2JOI/7Iozh73yHU0kLklJNHKVykZ9NYPKBmOcLHH8+CH/6Q1GsmekH58niZAwx9T7zwApVnn+0775XzriM7timrLnONylN/autWFv70J9nXuX7rKhCASNjY6SIRVCiECochHDZ5x4BDdr6V9cCw+I9/cAkqhU4bctLJFLq/j8QLLxJb/yAZCZ4cCw70moU6EDAu0gMDHmkIwYgjgEg1jmPUXSLZhMNG8hBnBFG5CUGI5Oe3D4lKrafHk3JEjbdvn+ewIJ5/UqeoAkXakeBfUesNDGQHhJa4VYclHIs5jcRLLwEmK7QazQZQ7JNambhF5yL18svsu3IUKXEmIxweIiDlpqVRkYh7LIQKmwzPKhwiML+WirM/zEG/e5h0ewexdeuIPfRbnO7u0tvd87Z5T6c9JwCRuETqkGNCPpIE1E8IolasqPDUaxUVxkFB4pLEoC+pgyQVjxDQvn2eHUZsM9InyXattZe8tKLCEKZsRyDxQMmk6UNDg5VwLCwEdbfcTPXqywjKdskjYXo2VpsdkFiX2QxJ70+RntkbNsCNNxE94wyqLl7FvK98mdTGjQw+sG5Y2XF5vO3axd5zzvFIyO+ZJ+lwAgGz8Iu009/vxUbFYp4jgQR4ij1HHAj27YPePDbKkLudgmS9BkMkYiOqq/MCUf2b3/X0mBREQphFwhKOxZxH+H3vK65gskjjdjlKOEoRWLCA6GmnefYbWbazbDnDbTo661yu7Ud7Zcm9Jqf+POWy6ncykHLtNq79ZsiGM9YM3+k0icceI/HYY6jKSir+xzlUXnQROplk8QvPMXjvvfTddvv4Pd6SSRgcRVUbVIZ0qqpg4UIvdqay0rPdSAaAnh5TZ2/vyBkXJJBVglYVLH788ZLJc8WKFSuKKWcJx2JOQsdipDo7ibz//cWVj8dp3DmCq2seFDvJ5gKc7m6cwQFqPn+Dz7aSY4NxP0dPWonT1+edExT6nOf7MPWn2ICiUVJtbVl2n6GygaBRjbmqMnw2HGBIwtGpNKSMU8GQ1JNKo9PGfkMqSeq1vzK4bj2pzZuHuqBjMWLr1hNbt57AggVUnn8elRdfRM0NNwAQOflkki+8MDZJcDSyAROX1DdgXt3dRvqoqTHSi6T9ka0dDvSW3gcAPTZ38dS7e4sqZwnHYk5i8MGHOPAPX6LqistZ8N3vjFpeVVRM2iSbC0j9aTP7Vl9eVNnGXTvYc9TRk9KPxl07eOess0u/MBCgccebvH3qB1GVFaiqahY/+nvzXWw4oTAqEkZVVRNZcSILvve/UdEosXXrGVy3nnRb21B1Tnc3A3fdzcBddxM85BAqL1pF3a1fh7pa4usfJLZuHanX/jpCh8aJZNrsveNP+BmPj5x6ZwbAEo7FnMTAXWaTtciKE6e5JxYzAq5Kzdmb/ZCQefPNvMUTzzxD3//5AaGj30fVxRez8O474UAPg+vXE3tgHZnd3g4tmd276f/x7fT/+HZCy5ZRddEqFv7sp+hkcoisMm+8MQljwkg7U4zAokU4EmBaIizhWMw5JF99ldTml1G1tVSuWjXd3bGYxUj/dQu9f91C7798i8jKlVRdvMr1XGs3ZPLQb9GSHBNIt7XR+53v0vud7xI+4QSqLr6Igx64DzIOqfY20m3tpNrc9/b2rGuLwaSl48lFOEzo8MMJNTcRamoi3NxEqKmZYNORBGpq6LrkYyQ3biq5D5ZwLOYcBtwtpKsuvZTAGLcSqP77TxE95RScri6c/fvJdHWRePIp0h0dE9lVi9kCrUm+8IKx0dx4M41vbCPY2Mj8r99C4ulniK1fT/x3v0fHvIzRqc2b6dm8mZ6bbibY2EhoWQvhlhYiJ5xA1WWXEV7WgnZjug7a8AjptnaSf9pE4qmnSbfn/59NdDqe4OGHEf3g6UROPIHwe5cRPMR4c75ny2tkdu0m3dlBuqOTxHPPM3DXL0l1dlB7882Empot4VhYOL29xFy31eqr1oy5nsgJJ5Devp10ZyeB+nqqr1yD091tCcdiKFXP3pNPJdDQwLwvfoGFP/p3Mj09JJ54gtgD64k/+WSWu3Bm1y4yu3aReOLJrKoCBy8m3LJsiIwqzz2XeV/4IoG6WnR/P5m33ybV3k5q88vEn35mbP0NBAguXQrAwjv+k9ARRxBctAhVU43u6yPd0Umqs5PYw/9NuqODdOc20m+8UdDdOd3ZSai5aUxdsYRjMacweO996FiMyKmnEm5uHnM9wYZ6Bn95D4lnnwUgesrJOF37JqqbswLh446jcePGkq9L7yjN26/UukdMnDnKtRMNp6uLnn+6kZprPsk7p51O5QXnU/PZz1D3/e8R/+1vGVy/3kgCBRZv5+29JN7eS+KZbDIJNDQQamkhvKyF0LIWKs46i5pPX4fWmob1D5Buayfd3k6qrZ10WxuZ3btRNTWEmpoINTcRbm42n5uaCB62FOfdd4k//gSZ7duJP7qB9LZO0h2dOO++W/KYM2++SeSSApsYjgJLOBZzCjoWQ9XUUHPVleOqJ1DfQGb/vqzvTlfXeLs3q5CKxdj1iU/As89CKkPjrh1Z6pzc71OBfDaIsfZrosnL6e5m4M67GLjzLuO5tupCam++iVBLC5kdO4bsNum2NmPH2ba9oOHd6eoi2dVF8rnnso6rujpDQi3LCC9rIXrWmYRbWlCLFkE8TnrbNiOldHQSe+ghUh2dpLdvg3ii5DEG3/MeQs3NhJqONO/NzYSbm6C2luQYpS1LOBZzCvOu/xzVn7zaxGGMA4H6hVkSTbB+YRYBlQUkY3BqGhJfTgEmkrxykdm9m/7bf0L/7T+BSIRQ05FDqrPK889jXvMXCC5dSmb3HtJtW0m3dxgSau8g3dExZNvJhT5wgOSLL5F88aXsExXRMZEK0SihI48k3NTkEUtLM6Ejj0T395Pu6CDVuY10e7uRjDo6jIfeGLNOWMKxmHMIVFePs4IAqq4OZ59fwqnH2bd/nD2bhejpme4ezH4kk6S3vE56y+vZx0MhQkccweInHyc+7w9UXnIJwYZ6dDiMs3Mnqa1tpNtd1Vl7O+n2DnShTQJHIZtAQ4PrcdbsepwZ1VugsRGVyeDs7ya9ayep17cSOfZYdh91NFqCdycQlnAs5gQyXV3EH3ucqgsvQI3RM00QqKszk82N3VC1tcb7aAxxB7MajruRmMXkIJ0m3d4OkB1UG4kQPeUkIiefQuS4Y6m84HwCBx0E0SjOnj2kt7a5BNTuuVf39hoCO+wwQyyuDSfsqsS04xBcuNBVsW0n+eor9P3sZyRfeHHY/7pmzScmhWzAEo7FHMHgr9bS+6/fJvHUUyz88W3jqivQ0JAl3QTr67O+lw20hrffme5elB+SSRJPPUPiqRw7iZstoecfv0aouZnIypVUX3EFwZZmSKVRVZWGkDo7SbV3kHzxRQZ/tZZ0RwdOd7fJ0nDGmdMyJIElHItZD53JMHD3LwETezNeGPtNl+97PZlyJJxAYLp7YOGHK3HHNzwGGx7LOhVYUIczMDjjpXBLOBazHoknniSzcyfBpUuJnnnGuOsLNjRkOQwEGurLziUamJ5dMmcppttd2+meHapPSzgWsx79bmaB6jWfMLs+jhNGovFLOA04+8rLJRqY/fvfTCEm0+NtqjEW8gy3thZVzhLOBEHH46iKihlXd+61M7Wu8ZRruPOOcbUhZf2TrPffvjf0OdhQT/WaNVSvyc5cUOwks7CYTSiZPK/7FJs2bSoqz40lnBykXnllTE8hYxWni8FYUucLcvs1U+sqhKloA2D3hRfCa1sAWPT4hmE2nAM33kTdP99q+nLG6fClLxU9yWYtJtKGEyB7u+Ro1EhQ8g7eu+OYz6mUUeslEhCLF7dNZyGEg6atQMBE/TsOxGe2vWPWoKGh6KKWcCYQkyUuj5fMcp9MZmJdhTAVbeQiML+WTFdODM5LvhQvTz0D1103rn7NCgQCZgviUvdYiYZh3rzs3SYdhm8y1tIEBx9sSOWPz49cZ0uTCUIdHIT9Y7BXpDKQytlC/IjDzB4ye8pnX6NJQQnZqy3hWFjkQFVXZ9lsgg0NWTYdoKR93GctgkE45hj486ulXXfccUaK6dwGe0dwqw5H4PDDjTQzGuEcfbSRSlIp+N0jo/ehdh70jBJLctRRxqurthZebxu5bFWFeZJ/a+fobU8ipts5IS+WLy+6aFGEo5T6CPADIAj8P631v+acjwJ3AiuAfcBqrfUb7rmvAtcCGeDzWuvfj1SnUuoIYC2wEPgTcKXWOjmRbRSLqtWriX7o9KLLL37hudELjRELbvvRmK/N7ddMrasQpqINPwLz52VlFcjrpTbebAZjwGTMw5EQDAbhAx+AbduK3+grEobjj4dQCBob4Tf3FS57/HI49FAjSR37fnj1L/nL1VTDEUcYcijWc+744+GtHbD9jcJljj3WbNVcXz864axYAYsXQ3MzPP5k4XLVVRCPme2gR8OCWqPmi0RGJ7KWJujtnVjnhPoFRl3ZPzh62RHQcPDBRZcdlXCUUkHgNuBvgZ3AS0qpB7XW/v1TrwW6tdbNSqmPA98GViuljgY+DhwDHAJsUEotc68pVOe3ge9rrdcqpX7i1n37BLdRFMLLj0PHYySKSFQXf6SIp65pwET2a6bWNRlt+LchCOQL/AwGx1z3WDAZ81BrPeLqHQgE4OSTYcsW6OzMX+iM041a7NBDzeJZXW3IRiloaYHTTjPfAwHveDBoiMONK0FruP5zMlBz3HE8ewsYySaVMmW/frMhn+7uwql3li+HM8+EBQuMei8QMO0Gg56NSGw5+/aZ/u/Zk7+uq6805+fNM/278EKvf0qZRdtxjMovHodXXjEknQ+HLTH3q77e3Ktw2LxXVnr/Ka3N/QmFTB8zGe914ADs3QtvvQVvjiCxVEbhve+FhQuN7QxMW0qZekMhr6143BxPJs3nZBL6+2Hnzmy1aD6c91HC4XDh8zkoRsJZCXRorbcBKKXWAqsA/x99FXCL+/le4EdKKeUeX6u1TgDblVIdbn3kq1MptQX4MHCFW+YXbr23T1QbOf0eFak/v0Js/YOlXGIxl6AUqrYWp7s7+7gz5XvHT8Y8HF38O+oo+NCHzAKZDxdcAIsWQVWVWcT8ixqYRVmcAZQy71p7DgmO4x2XdznuJwatzctxzPdEwiyOhQjn6KONCiwSMS8hOv+iLoSxdCns3w/vFFD/feADZnwVFWZ84bCpJxw2dch/IR6HgQFDTlu35q/rox8192vePK+uaNS7Z4GA6a9A+izOE4mEGf++ffDmm+ZhIB+uucaQbWWlqVf6LmOW30UpU7dS3r1OJs04BgZMG4WI+MTj4ZxzzINJkSiGcBoBP5XuBE4qVEZrnVZK9QD17vHnc65tdD/nq7MeOKC1TucpP1FtDINS6jrgOoDjSmBri7mPirPPJv366zMhCHKy5mEW/HNh6dKlVMyfT/ykk+Cgg/L3qrXVLJjB4NDCE3Q/y3elFFrrrO9uHwsOVmuN4zhDZbTWZDIZMqlUNhkVyKocPukkotEo4XAYrbVRD/ralrrl5SxZwuBgAdVSayvBcJhIJIJSamg8SqmhMTmOY/qXyaC1Jn3iifnruvRSz1suFCIYCg27V4FAYKif/rodl9i01uY+ZDKFyeCyy6CigkAolPV7+O+nfxz++5HJZNDJpCGfWAwK2X4uvdTY60pAMYSj8hzL/acUKlPoeD5KHKn8RLYx/KDWPwV+CrA8EskqU7X6MiInrcx3mcUcR6BmHuHWVro/+7nhJxcunOruTMY8HH7QNxdaW1t1fX09B4JBBg45JG+nGltaCnZ4OrFo0aKSr5k/f37e440leGGNhsb3vW/C6gKM00W+dibwd3GOOSb/iVNP5aDDDiuprmIIZyfgv+OHArsLlNmplAoBtcD+Ua7Nd7wLqFNKhVwpx19+otooGoP/9WuTTbUILPzxj+j/+X+WUn3RqPm7a9j/uRvGdG1uv2ZqXYUwFW0UQtVFFwHQ8KtfDjsXLLA4TSImax6Oirq6Ompra4HJjTcbDyayXzO1rulqR6Sv3DbGQmpqJLEWwP3jtgFnA7uAl4ArtNav+cpcDxyrtf6Ma6y8RGt9mVLqGOAejL74EOAxoAXzxJW3TqXUb4D7fE4Dr2itfzyRbYw03uWRiH74oMXF3T0fJjNtxXjqnsj0GpNZ13jLTfS1xdStlNqktZ6SdAOTMQ9HcxpobW3VG8ewxbRF+aHYuTCqhOPqgm8Afo9xx/y5Swy3Ahu11g8C/wHc5Roj92M8YnDL/Rpj2EwD18ufPF+dbpNfBtYqpb4BbHbrZoLbKIgd6TTnvlt6IFi4tZVNu3dOSuT5itbWFakx9AmG92um1lUIU9FGEWjASN8ejI69NH3CODBZ83AkbNq0qUsp9WaeU8PvR3mhnMdfaOxFzYVRJRwLA6XUxql6mp1pKOexgx1/Lsr9fpTz+Mc7drvhhYWFhYXFlMASjoWFhYXFlMASTvH46XR3YBpRzmMHO/5clPv9KOfxj2vs1oZjYWFhYTElsBKOhYWFhcWUwBKOhYWFhcWUwBLOKFBKfUQptVUp1aGU+sp092cqoJR6Qyn1qlLqZaXURvfYQqXUo0qpdvd9wXT3cyKglPq5UuodpdRffMfyjlUZ/ND9L7yilCqQMGvuotzmQznNBZj8+WAJZwQoLyX8R4GjgcuVSfVeDjhLa328z+f+K8BjWusWTKT6XFls7gA+knOs0Fg/isli0YJJcHn7FPVxRqCM50O5zAWY5PlgCWdkDKWE11onMRvDrZrmPk0XVmG2i8B9v2ga+zJh0Fo/jYnK96PQWFcBd2qD5zF5/94zNT2dEbDzwWBOzgWY/PlgCWdk5EsJnzet+xyDBh5RSm1y09UDLNZa7wFw30tPxzt7UGis5fp/EJTj+Mt9LsAEzoeitpguYxSd1n2O4TSt9W6l1CLgUaXU69PdoRmCcv0/CMpx/HYuFEbJ/wcr4YyMcaV1n63QWu92398BHsCoUvaKuOy+F9gecU6g0FjL8v/gQ9mN384FYALngyWckfES0KKUOkIpFcFk353T+00rpaqVUvPkM3AO8BfMuK92i10NrJ+eHk4JCo31QeAq1zvnZKBHVA1lgrKaD3YuDGHi5oN/m1X7Gv4CzsXsQ9IJfG26+zMF4z0S+LP7ek3GjNmq+DGg3X1fON19naDx/grYA6QwT2zXFhorRoVwm/tfeBVone7+T8P9Kpv5UG5zwR3bpM4Hm9rGwsLCwmJKYFVqFhYWFhZTAks4FhYWFhZTAks4FhYWFhZTAks4FhYWFhZTAks4FhYWFhZTAks4FhYWFhZTAks4FhYWFhZTgv8Pd/hfTzKNUSoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models['r, smr'] = model\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O0QEA6Bk68jT1P0uaK2mXtP5tSf8ZETfbvkbS2ZJ+mN5XR8R+tk9N6U6pYJlrXqVmcCoeSKS9PLfV7FQAgI4rq2Vsey9Jx0q6Lq1b0iRJt6UkN0qanJZPSOtK+w9L6QEAQAnldlNfLunfJTWn9cGS1kREU1pfLKkhLTdIWiRJaf/alB4AAJTQbje17eMkrYiI2bY/UthcImmUsa8436mSpkrS3nvvXVZha1k1uomLx7gulX+lusMBANVVTsv4ryQdb7tR0s3KuqcvlzTQdiGY7yVpaVpeLGm4JKX9u0p6vWWmEfGjiBgXEePq6+u7VAkAAGpZuy3jiLhA0gWSlFrGn4+I02z/UtJJygL0mZLuTIfMSOuPpv0PRMRWLeM8FIaPrPYMSgAAdERXfmf8BUnn216g7J7wT9L2n0ganLafL+mLXSsiAAA9W4cmioiIhyQ9lJYXShpfIs1bkk6uQNl6hCUNw1vd17Bk0TYsCQCgu9puZ21a8fR5ktp/sKqtYAoAQCUwHCYAADnbblvGnVGJbmVa2gCAlmgZAwCQM4IxAAA5IxgDAJAzgjEAADkjGAMAkDOCMQAAOSMYAwCQM4IxAAA5IxgDAJAzgjEAADkjGAMAkDOCMQAAOSMYAwCQM4IxAAA5IxgDAJAzgjEAADkjGAMAkDOCMQAAOSMYAwCQM4IxAAA5IxgDAJAzgjEAADkjGAMAkDOCMQAAOSMYAwCQM4IxAAA5IxgDAJAzgjEAADkjGAMAkDOCMQAAOSMYAwCQM4IxAAA5IxgDAJAzgjEAADkjGAMAkLN2g7Ht/rafsP2s7Tm2L0rbR9p+3PZ827fY7pu290vrC9L+EdWtAgAAta13GWn+LGlSRKy33UfSw7bvkXS+pP+MiJttXyPpbEk/TO+rI2I/26dK+rakU6pU/pKWNAwvveMH7y8/LQAA20i7LePIrE+rfdIrJE2SdFvafqOkyWn5hLSutP8w265YiQEA6GHKumdsu5ftZyStkHSfpBclrYmIppRksaSGtNwgaZEkpf1rJQ2uZKEBAOhJyummVkRslnSw7YGSbpe0f6lk6b1UKzhabrA9VdJUSdp7773LKmxnNCxZ9O7KHce2vR8AgBx06GnqiFgj6SFJEyUNtF0I5ntJWpqWF0saLklp/66SXi+R148iYlxEjKuvr+9c6QEA6AHKeZq6PrWIZXsHSYdLmivpQUknpWRnSrozLc9I60r7H4iIrVrGAAAgU0439Z6SbrTdS1nwvjUi7rL9vKSbbV8s6WlJP0npfyLpZ7YXKGsRn1qFcgMA0GO0G4wj4g+SxpbYvlDS+BLb35J0ckVKBwDAdoARuAAAyBnBGACAnBGMAQDIGcEYAICcEYwBAMgZwRgAgJwRjAEAyBnBGACAnBGMAQDIGcEYAICcEYwBAMgZwRgAgJwRjAEAyBnBGACAnBGMAQDIGcEYAICcEYwBAMgZwRgAgJwRjAEAyBnBGACAnBGMAQDIGcEYAICcEYwBAMgZwRgAgJwRjAEAyBnBGACAnBGMAQDIGcEYAICcEYwBAMgZwRgAgJwRjAEAyBnBGACAnBGMAQDIGcEYAICcEYwBAMgZwRgAgJz1zrsAHXHtCTe9s3zunX+fY0kAAKgcWsYAAOSMYAwAQM7aDca2h9t+0PZc23Ns/3PaPsj2fbbnp/fd0nbbvtL2Att/sP2BalcCAIBaVk7LuEnSv0bE/pImSppm+wBJX5T024gYJem3aV2SjpY0Kr2mSvphxUsNAEAP0m4wjohlEfFUWl4naa6kBkknSLoxJbtR0uS0fIKkn0bmMUkDbe9Z8ZIDANBDdOiese0RksZKelzS7hGxTMoCtqShKVmDpEVFhy1O21rmNdX2LNuzVq5c2fGSAwDQQ5QdjG3vLOlXkj4XEW+0lbTEtthqQ8SPImJcRIyrr68vtxgAAPQ4ZQVj232UBeKfR8Sv0+ZXC93P6X1F2r5Y0vCiw/eStLQyxQUAoOdpd9AP25b0E0lzI+Kyol0zJJ0p6dL0fmfR9s/avlnSBElrC93Z2NKShuHtJ2rLD95fmYIAAHJVzghcfyXpdEl/tP1M2vYlZUH4VttnS3pF0slp328kHSNpgaQ3Jf1DRUsMAEAP024wjoiHVfo+sCQdViJ9SJrWxXIBALDdqKmxqXuChiWL2k9UrjuOrVxeAIDcMBwmAAA5IxgDAJAzuqkBAJ02e/bsob17975O0kGigdeaZknPNTU1nXPIIYesKJWAYAwA6LTevXtft8cee+xfX1+/uq6ubqsBniA1Nzd75cqVByxfvvw6SceXSsO3GABAVxxUX1//BoG4dXV1dVFfX79WWe9B6TTbsDwAgJ6njkDcvnSNWo25dFMDACpiScPwQ6qVd8OSRbOrlXd3QMsYAICcEYwBAMgZ3dQAgIqrRLdyud3e8+bN63vUUUeNGjt27Ibnnntux3333fetX/7yl40DBgxo7moZthVaxgCAmtfY2Nj/M5/5zMoXXnjh+QEDBjR/97vfrc+7TB1BMAYA1Lw99tjj7SOOOGKDJJ1++umvPfLIIzvnXaaOIBgDAGqe7TbXuzvuGQMAKq6aP3MqZdmyZX3vv//+nQ4//PAN06dPH3TooYeu35bn7ypaxgCAmrfvvvu+df311w8ePXr0AatXr+79+c9/fmXeZeoIWsYAgJpXV1en6dOnv5J3OTqLYAwAqIiePkpWNdFNDQCoaWPGjHl7/vz5c/IuR1cQjAEAyBnBGACAnBGMAQDIGQ9wAQAqYuKF91btt8WPXXRkj344jJYxAKCmjR079r15l6GrCMYAgJr29NNP/6kj6ZuamqpVlHds2rSpQ+nppgYAVFwlupXL7fbecccdx7755ptPt5dm6tSprz7wwAO7fPe731185JFHlhwu8/rrr9/tkksuGVZXVxcDBgzYPGvWrHlXXnnl4BkzZgxsbm72vHnzdpg2bdryt99+u+6WW24Z3Ldv3+aZM2fO33333TePHz9+zPjx49c//vjjOx9zzDFrLrroolfLrSvBGADQ423cuLHuoIMO2nj55ZcvbSvdpZdeuufMmTNfGDly5KZVq1b1Kmx/4YUXdnj22Wef37hxY92YMWMO+upXv7pk7ty5z5999tnDr7322sFf+9rXVkjSmjVrej355JPzOlo+uqkBAD1er169dNZZZ61uL924cePWn3baaSO+973vDSnuzj700EPX7bbbbs3Dhg1r2nnnnTeffPLJayTpfe9735uNjY39CummTJnyemfKRzAGAPR4ffv2be7du/3O4OnTp79y8cUXL120aFHfgw8++MDly5f3SsdHIU1dXZ369+8fheWmpqZ35mscMGBAc2fKRzc1AKDiqvkzp7aMHDnywJdeeqndoTGnTZvWMGHChA1nnHHGmuLtc+bM6Tdp0qQNkyZN2nDvvfcOXLhwYd/qlfZdtIwBAD3CsmXLekeE208pPf/88zsMGzZsq0ee/+Vf/mWv0aNHHzBq1KgDJ06cuG7ixIkbK1/SrdEyBgDUtMKT1A899NBO55xzzoq20hRs2rTJhx9++IaW6WbOnPliy23nnXfea5JeK6wvWbLkj6X2PfHEEx1+cKuAYAwAqIi8R8maMmXK2nLTPvzww/OrWZaOIhhvJyZeeO82P+djFx25zc8JALWIe8YAAOSMlnEPcfwdx261bejYyp5jxdPnVTZDAIAkgjE6oJxu5zy6wwGg1hGMAQAVcfwdx1btt8UzJt/do6dQbDcY275e0nGSVkTEQWnbIEm3SBohqVHSJyJitW1LukLSMZLelHRWRDxVnaLjh9P+8M5yw5JFVTlHqe5vAOhOxo4d+96OztzU3ZTzANd/STqqxbYvSvptRIyS9Nu0LklHSxqVXlMl/bAyxQQAoLRaD8RSGS3jiPg/2yNabD5B0kfS8o2SHpL0hbT9pxERkh6zPdD2nhGxrFIFBgB0f5XoVi6327ucKRS7u87+tGn3QoBN70PT9gZJxf2li9O2rdieanuW7VkrV67sZDEAAKh9lf6dcakxQaPENkXEjyJiXESMq6+vr3AxAACoHZ0Nxq/a3lOS0nthLNDFkoYXpdtLUpsTOQMAsL3r7E+bZkg6U9Kl6f3Oou2ftX2zpAmS1nK/GAC2P9X8mVNPVM5Pm36h7GGtIbYXS7pQWRC+1fbZkl6RdHJK/htlP2taoOynTf9QhTIDANCjlPM09ZRWdh1WIm1ImtbVQgEAUK5af5JaYgQuAECF9PRRsqqJWZsAAMgZwRgAgJwRjAEAyBnBGACAnPEAFwCgIq494aaq/bb43Dv/vkc/HEbLGACAnBGMAQA9QnNzszZv3lz18zQ1NVU8T7qpe4glDcPbT1SmhiWL2k8EAG2oRLdyOd3e8+bN63v00UePOvTQQ9fNnj175zvvvHPB6NGj3y6V9uKLLx56ww031Pfq1StGjx791l133bXw/PPPH9bY2Nj31Vdf7dPY2Nj/W9/61qJHH3105wceeGCX3XfffdP999+/oF+/ftHQ0PC+KVOmrHrwwQd3Offcc1dMnTp1dVfrV4xgDACoaY2Njf1//OMfN950002vtJXuyiuv3OPll1/+4w477BCrVq3qVdj+8ssv93vkkUdeeOqpp/pPmjTpvTfeeOOL11xzzeKPfvSj77n11lt3Pf3009dIUv/+/Ztnz549rxp1oJsaAFDT9txzz7cPO+ywDe2lGzNmzMYTTzxx5NVXXz2oT58+70zve/jhh6/t169fjB8/fuPmzZt90kknvSFJBx544MaXXnqpbyHdGWecUdHWcDFaxjWskt3JlezmBoBtaccdd2wuJ92DDz44/5577hlwxx13DPzOd74zbP78+c9JUr9+/UKSevXqpd69e0ddXdZOraurU1NTkwvHDxgwoKzzdAbBGABQcdX8mVN7TjzxxBHnnXfeir/92799s7Bt8+bNevHFF/t+7GMfW3fEEUesHzZs2KC1a9f2aiufbYlgDADoUebOnbvj8OHDNxVva2pq8ic/+cmR69at6xURPvfcc18dMmRI9R+9LhPBGABQs8aMGfP2/Pnz5xTWX3/99bqRI0e+td9++20RjPv16xelHr667LLLlhavF0/HWLxvyZIlf6xsybdEMAYAVER3GCVr0KBBzffcc8/CvMvRUTxNDQBAzgjGAICuaG5ubnb7ybZv6Rq1+jQ23dQo2/F3HNtumqFji9Nf2anzzJh8d6eOA5CL51auXHlAfX392rq6umg/+fanubnZK1eu3FXSc62lIRgDADqtqanpnOXLl1+3fPnyg0Rva2uaJT3X1NR0TmsJCMYAgE475JBDVkg6Pu9y1DqCMdrU0S7jiRfe+87yYxcdWfZx5XSBA0BPRZcCAAA5IxgDAJAzuqmxlS5NGnHOdVvkw9zIANA+WsYAAOSMYAwAQM7opoakCs6NXPQ09cfPuW6L9bYUDxYyscxjSunIE9wA0F3QMgYAIGcEYwAAckY3NSrqsYuO3OJp7HK7v4vHse5oV3NXurUBoDugZQwAQM4IxgAA5IxuanQ7HR2nurPTNjJVI4DugpYxAAA5IxgDAJAzuqnRLXSly7gj0zYyVSOA7ohgjKrq0qQTLZTzM6n2fuZUqZG+2jN0bPn3riuB+99AbatKMLZ9lKQrJPWSdF1EXFqN82D70mpgL5opqiO2dcBE27Z1rwVfYNCdVDwY2+4l6QeSPippsaQnbc+IiOcrfS4AGbrfuybvgWMYUx3VaBmPl7QgIhZKku2bJZ0giWC8najkHMbldHP/6rpzys7v//3g/V0pTqf8cNofKpLPx1v0ANCy75q8AzBQzBFR2QztkyQdFRHnpPXTJU2IiM+2SDdV0tS0OkbSvDJPMUTSqgoVt7ugTrWBOtWGnlgnqXS99omI+jwKg8qqRsvYJbZtFfEj4keSftThzO1ZETGuMwXrrqhTbaBOtaEn1knqufVCphq/M14sqbhvcS9JS6twHgAAeoRqBOMnJY2yPdJ2X0mnSppRhfMAANAjVLybOiKabH9W0r3Kftp0fUTMqeApOty1XQOoU22gTrWhJ9ZJ6rn1gqrwABcAAOgYxqYGACBnBGMAAHKWazC2fZTtebYX2P5iif39bN+S9j9ue0TRvgvS9tpZ2JUAAAO7SURBVHm2jyw3z2qrUp0abf/R9jO2Z22bmmxR5k7VyfZg2w/aXm/7qhbHHJLqtMD2lbZL/SSuqqpUr4dSns+k19BtU5t3zt/ZOn3U9uz0mcy2PanomFw/qyrVqVY/p/FFZX7W9onl5oluLiJyeSl7uOtFSftK6ivpWUkHtEjzj5KuScunSrolLR+Q0veTNDLl06ucPGutTmlfo6QhNfg57STpQ5I+I+mqFsc8Iekvlf0u/R5JR/eQej0kaVwNflZjJQ1LywdJWtIdPqsq1qlWP6cdJfVOy3tKWqHsQdxc//bx6vorz5bxO8NmRsTbkgrDZhY7QdKNafk2SYelb+UnSLo5Iv4cES9JWpDyKyfPaqpGnfLW6TpFxIaIeFjSW8WJbe8paZeIeDSyvyo/lTS5qrXYWsXr1Q10pU5PR0RhPIA5kvqn1lnen1XF67RNSt22rtTpzYhoStv7690BlfL+24cuyjMYN0gqHsR4cdpWMk36B7hW0uA2ji0nz2qqRp2k7D/czNTVNlXbVlfq1Faei9vJs9qqUa+CG1I34le3cZduper0cUlPR8Sflf9nVY06FdTk52R7gu05kv4o6TNpf95/+9BFeQbjcobNbC1NR7dvK9WokyT9VUR8QNLRkqbZ/pvOF7HDulKnruRZbdWolySdFhHvk/TX6XV6J8rWWV2uk+0DJX1b0rkdyLOaqlEnqYY/p4h4PCIOlPRBSRfY7l9mnujG8gzG5Qyb+U4a270l7Srp9TaOzXsozmrUSYWutohYIel2bdvu667Uqa0892onz2qrRr0UEUvS+zpJ01VDn5XtvZT9+zojIl4sSp/nZ1WNOtX051QQEXMlbVB2Pzzvv33oojyDcTnDZs6QdGZaPknSA+m+1QxJp6Z7WiMljVL2kEneQ3FWvE62d7I9QJJs7yTpCEnPbYO6FHSlTiVFxDJJ62xPTN2DZ0i6s/JFb1PF62W7t+0habmPpONUI5+V7YGS7pZ0QUT8vpC4G3xWFa9TjX9OI1Nwlu19lM1411hmnujO8nx6TNIxkl5Q9hTgl9O2/5B0fFruL+mXyh5mekLSvkXHfjkdN09FT3eWyrOW66Ts6chn02tODdapUdk3+vXKvr0fkLaPU/YH8EVJVymNBlfL9VL2lPVsSX9In9UVSk/Ed/c6SfqKslbWM0Wvod3hs6p0nWr8czo9lfkZSU9JmtxWnrxq58VwmAAA5IwRuAAAyBnBGACAnBGMAQDIGcEYAICcEYwBAMgZwRgAgJwRjAEAyNn/BzLjqPwdD0z7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, label in enumerate(['p', 'i, r, smr', 'i', 'r, smr']):\n",
    "    try:\n",
    "        plt.hist(models[label].vars['p']['mu_age'].trace().mean(1), density=False, histtype='step',\n",
    "             color=dismod_mr.plot.colors[i%4], linewidth=3, linestyle=['solid','dashed'][i//4],\n",
    "             label=label)\n",
    "    except AttributeError as e:\n",
    "        print(e)\n",
    "plt.legend(loc=(1.1,.1))\n",
    "plt.title('Posterior Distribution Comparison\\nCrude Prevalence')\n",
    "plt.axis(xmin=-.001);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Jul 23 16:08:19 PDT 2019\r\n"
     ]
    }
   ],
   "source": [
    "!date"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dismod_mr",
   "language": "python",
   "name": "dismod_mr"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
